System and device for analyzing a fluidic sample

ABSTRACT

A device for analyzing an analyte in a sample includes a first substrate, a second substrate, a fluidic channel, an inlet port and an outlet port. Each of the first substrate and the second substrate has an inner surface and an outer surface, the inner surface of the first substrate forming, at least in part, the lower wall of the fluidic channel, and the inner surface of the second substrate forming, at least in part, the upper wall of the fluidic channel. The fluidic channel is connected to the inlet port and the outlet port. The fluidic channel includes a reagent region and a detection region, at least a portion of the reagent region being coated with one or more dried reagents. The device further includes a wicking pad located on the outer surface of the second substrate, the wicking pad being positioned at a pre-determined distance from the outlet port.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is filed as a continuation-in-part of PCT applicationPCT/US2011/055844 filed Oct. 11, 2011, which claims priority to U.S.Provisional Patent Application Ser. No. 61/391,911, filed Oct. 11, 2010.This application also claims priority to U.S. Provisional PatentApplication Ser. No. 61/719,812, filed Oct. 29, 2012, and U.S.Provisional Patent Application Ser. No. 61/732,858, filed Dec. 3, 2012.The above-identified patent applications are incorporated herein byreference in their entireties.

U.S. GOVERNMENT RIGHTS

This invention was made with Government support under NIH Grant Nos.AI070052 and AI0068543, both awarded by the National Institutes ofHealth. The Government has certain rights in this invention.

BACKGROUND

Identification and enumeration of analytes in complex sample matricesare used in medical, biological, industrial, and environmentalapplications. Example analytes include particles such as viruses,bacteria, parasites, and specific cell types, typically found in acomplex matrix of confounding substances. Sample preparation methodssuch as filtration, lysis, homogenization and dilution are oftenrequired to enable specific particle identification and enumeration inthese complex matrices. Particle identification and enumeration areoften based on expensive, laboratory-based measurement devices orinstrumentation.

A useful example is the identification and enumeration of CD4+T-helperlymphocytes (CD4 cells) for monitoring and managing conditions inpersons with HIV/AIDS. HIV mediated CD4 cell destruction is the centralimmunologic feature of HIV infection. Thus, the CD4 count is a criticalmeasurement in initial assessment of infection and disease staging, inmonitoring antiretroviral therapy and in managing primary and secondaryprophylaxis for opportunistic infections. In fact, quantitative T helpercell counts in the range of 0 to 1000 cells per microliter are acritical indicator for initiating and optimizing anti-retroviraltreatment and preventing viral drug resistance. Flow cytometry is thecurrent standard-of-care for CD4 cell counting. Unfortunately, flowcytometry is a central lab-based technique; transport, equipment, andoperational costs render the technique cost-prohibitive in limitedresource settings where HIV prevalence is highest.

SUMMARY

In an embodiment, a particle identification system includes: a cartridgefor containing a sample with fluorescently labeled particles;illumination for illuminating a region within the cartridge to stimulateemission from fluorescently labeled particles in the region; imager forgenerating wavelength-filtered electronic images of the emission withinat least one measurement field of the region; and particle identifierfor processing the electronic images to determine a superset ofparticles of interest and determining fluorescently labeled particleswithin the superset based on properties of the fluorescently labeledparticles in the at least one measurement field.

In an embodiment, a method determines fluorescently labeled particleswithin a sample, by: processing at least one electronic image from atleast one focal position within the sample; determining dimmestseparation lines between brighter areas in the electronic image; and,for each of the brighter areas, determining local background level basedon pixel values of the separation lines forming a perimeter therearound,to determine each of the fluorescently labeled particles.

In an embodiment, a system determines fluorescently labeled particleswithin a sample and includes: means for processing at least oneelectronic image from at least one focal position within the sample;means for determining dimmest separation lines between brighter areas inthe electronic image; and, for each of the brighter areas, means fordetermining local background level based on pixel values of theseparation lines forming a perimeter therearound, to determine each ofthe fluorescently labeled particles.

A software product comprising instructions, stored on computer-readablemedia, wherein the instructions, when executed by a computer, performsteps determining fluorescently labeled particles within a sample, theinstructions comprising: instructions for processing at least oneelectronic image from at least one focal position within the sample;instructions for determining dimmest separation lines between brighterareas in the electronic image; and, for each of the brighter areas,instructions for determining local background level based on pixelvalues of the separation lines forming a perimeter therearound, todetermine each of the fluorescently labeled particles.

In an embodiment, a cartridge is provided for detecting target analytesin a sample. The cartridge includes an inlet port and fluidic channelwith a detection region, and a dried reagent coating, disposed in thecartridge, for rehydrating into the sample upon input through the inletport for the detection region.

In an embodiment, a device for analyzing an analyte in a sample includesa first substrate, a second substrate, a fluidic channel, an inlet portand an outlet port. Each of the first substrate and the second substratehas an inner surface and an outer surface, the inner surface of thefirst substrate forming, at least in part, the lower wall of the fluidicchannel, and the inner surface of the second substrate forming, at leastin part, the upper wall of the fluidic channel. The fluidic channel isconnected to the inlet port and the outlet port. The fluidic channelincludes a reagent region and a detection region, at least a portion ofthe reagent region being coated with one or more dried reagents. Thedevice further includes a wicking pad located on the outer surface ofthe second substrate, the wicking pad being positioned at apre-determined distance from the outlet port.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a particle identification system that is configuredto receive and analyze a sample that is contained within a cartridge, inan embodiment.

FIG. 2 shows a schematic cross sectional view of a particleidentification system, in an embodiment.

FIG. 3 shows an enlarged, schematic cross sectional view of a portion ofthe system of FIG. 2, with certain structure removed for clarity.

FIG. 4 schematically shows a region immediately surrounding ameasurement field within the system of FIG. 2,

FIG. 5 shows details of structures at and surrounding the measurementfield depicted in FIG. 4.

FIG. 6 schematically shows a portion of a cartridge, formed of upper andlower elements, in an embodiment.

FIG. 7 is a detail view of a portion of the cartridge shown in FIG. 6.

FIG. 8 shows a schematic cross sectional view of a particleidentification system, in an embodiment.

FIG. 9 is a schematic block diagram of a particle identification system,in an embodiment.

FIG. 10 is a flowchart of a method for determining fluorescently labeledparticles within a sample, in an embodiment.

FIG. 11 is a schematic block diagram of a particle identificationsystem, in an embodiment.

FIG. 12 is a flowchart of a method of determining fluorescently labeledparticles in a sample, in an embodiment.

FIG. 13 is a flowchart of an exemplary method for gathering andprocessing data in a particle identification system, in an embodiment.

FIG. 14 is a flowchart of one exemplary autofocus method for optimizingoptical focus of a particle identification system on a cartridge, in anembodiment.

FIG. 15 is a flowchart of a subroutine for calculating autofocusposition that may be utilized as part of the method of FIG. 14, in anembodiment.

FIG. 16 is a flowchart of a subroutine for calculating a metric forevaluating autofocus quality, in an embodiment.

FIG. 17 is a flowchart of a flat field mask subroutine, in anembodiment.

FIG. 18A depicts a kernel that may be convoluted with image data to helpidentify particles, in an embodiment.

FIG. 18B depicts a normalized version of the kernel of FIG. 18A, thatmay also be convoluted with image data to help identify particles, in anembodiment.

FIG. 19 is a flowchart of an exemplary method for processing a sourceimage to identify particles within the image, in an embodiment.

FIG. 20 is a flowchart of an exemplary method for prefiltering a sourceimage to facilitate detection of particles therein, in an embodiment.

FIG. 21 is a flowchart of an exemplary method for establishing watershedlines between blobs in an image, and removing background from the image,in an embodiment.

FIG. 22 is a flowchart of a subroutine for providing watershed lines ina watershed image, based on an input image, in an embodiment.

FIG. 23 is a flowchart of a subroutine for morphological reconstructionof an input image, in an embodiment.

FIG. 24 is a flowchart of a subroutine for declumping a background imagebased on a second image, in an embodiment.

FIG. 25 is a flowchart of a subroutine for morphological intersection ofan image with a marker image, in an embodiment.

FIG. 26 is a flowchart of a subroutine that creates a list of blobsidentified within an image, in an embodiment.

FIGS. 27A through 27C are flowcharts of a subroutine that filters a listof detected blobs to provide a modified list of blobs, in an embodiment.

FIGS. 28 and 28B are flowcharts of a subroutine that correlates sourceimages from two lists of blobs to create a correlated list of blobs, inan embodiment.

FIGS. 29A and 29B are flowcharts of a subroutine that filters acorrelated list of blobs based on differences of position betweencorrelated blobs in the list, in an embodiment.

FIGS. 30A and 30B are flowcharts of a subroutine that filters blobs of acorrelated blob list based on correlation of low intensity blobs to amain population of the blobs.

FIG. 31 is a cross-sectional view of a fluidic cartridge, in accordancewith an embodiment.

FIG. 32 shows the fluidic cartridge of FIG. 31, illustrating liquid flowinto the fluidic cartridge.

FIG. 33 shows the fluidic cartridge of FIGS. 31 and 32 at the pointwhere the outlet port capillary valve stops liquid flow.

FIG. 34 shows the fluidic cartridge of FIGS. 31 and 32, shown here afterthe surface tension at the outlet port has been broken as the pressureat the outlet port has exceeded burst pressure.

FIG. 35 shows the fluidic cartridge of FIG. 32, this time including atilt for altering the pressure differential between the inlet port andthe outlet port.

FIG. 36 is a cross-sectional view of a fluidic cartridge including awicking pad, in accordance with an embodiment.

FIG. 37 shows the fluidic cartridge of FIG. 36, illustrating the liquidflow in the fluidic cartridge with the wicking pad.

FIG. 38 shows the fluidic cartridge of FIGS. 36 and 37, shown here afterthe surface tension at the outlet port has been broken as the pressureat the outlet port has exceeded burst pressure such that the liquid,upon contacting the wicking pad, is absorbed into the wicking pad.

FIG. 39 is a cross-sectional view of a fluidic cartridge including acombination of a wicking pad and a rail, in accordance with anembodiment.

FIG. 40 shows the fluidic cartridge of FIG. 39, illustrating liquid flowwithin the fluidic cartridge and the effect of capillary action as theliquid is drawn along the rail.

FIG. 41 shows the fluidic cartridge of FIGS. 39 and 40, illustrating theeffect of capillary action as the portion of the liquid along the railis absorbed into the wicking pad.

FIG. 42 shows the fluidic cartridge of FIGS. 39-41, illustrating theconsequent effect of capillary action along the rail as liquid is drawnalong the rail.

FIG. 43 shows an exploded view of an exemplary fluidic cartridge, inaccordance with an embodiment.

FIG. 44 shows an elevated view of a portion of the exemplary fluidiccartridge of FIG. 43, shown here to illustrate assembly of the uppercomponent and wicking pad, in accordance with an embodiment.

FIGS. 45 and 46 are schematic, isometric views of a cartridge foracquiring and/or processing a whole blood sample, in an embodiment, witha lid thereof shown in an open position and a closed position.

FIGS. 47 and 48 are schematic cross-sectional views of the cartridgeshown in FIGS. 7 and 8.

FIG. 49 shows an exploded view of a cartridge, in an embodiment.

FIG. 50 shows an exploded view of a cartridge, in an embodiment.

FIGS. 51A and 51B schematically illustrate a cartridge, in anembodiment.

FIGS. 52A and 52B schematically illustrate a cartridge that results fromthe cartridge of FIGS. 52A and 52B after sufficient time for reagentspots to spread, merge and dry, in an embodiment.

FIGS. 53A and 53B schematically illustrate a cartridge that has aD-shaped dried reagent coating, in an embodiment.

FIGS. 54A and 54B schematically illustrate a cartridge that has a driedreagent coating that is located within a fluidic channel, in anembodiment.

FIG. 55 shows results comparing a CD4 count from assembled cartridges toresults obtained from a reference flow cytometer, in an embodiment.

FIGS. 56A and 56B schematically illustrate the cartridge of FIGS. 54Aand 54B after addition of a blood sample.

FIGS. 57A and 57B schematically illustrate a cartridge having a driedreagent region that is poorly formed.

FIGS. 58A and 58B schematically illustrate a cartridge in which reagentrehydration is too rapid with respect to fluid flow, or the cartridgedoes not contain enough dried reagent.

FIGS. 59A and 59B schematically illustrate a cartridge after addition ofa blood sample in which reagent rehydration is too slow with respect tofluid flow.

FIGS. 60A and 60B are schematic representations of a liquid sample beingheld in an inlet port of a cartridge, in an embodiment.

FIGS. 61A and 61B show the liquid sample being drawn into a fluidicchannel of the cartridge of FIGS. 60A and 60B, in an embodiment.

FIGS. 62A and 62B are cross-sectional illustrations showing a cartridgethat has features to hold and release a liquid sample into a fluidicchannel, in an embodiment.

FIG. 63 shows results comparing a CD4 count from assembled cartridgeshaving features to hold and release a liquid sample into a fluidicchannel, to results obtained from a reference flow cytometer, in anembodiment.

FIGS. 64, 65 and 66 show average signal recorded in both fluorescencechannels, as well as the number of T-helper cells for each measurementfield, for each of three cartridges, in an embodiment.

DETAILED DESCRIPTION OF DRAWINGS

The present disclosure may be understood by reference to the followingdetailed description taken in conjunction with the drawings brieflydescribed below. It is noted that, for purposes of illustrative clarity,certain elements in the drawings may not be drawn to scale. Specificinstances of an item may be referred to by use of a numeral inparentheses (e.g., 16(1)) while numerals without parentheses refer toany such item (e.g., 16).

The present disclosure is divided into the following main sections forclarity: System Level Overviews; Particle Counting Methods and Software;Fluidic Features and Methods; and Cartridge Features and Methods.

I. System Level Overviews

The methods described here may be collectively referred to as “staticcytometry” using an inventive implementation of an optical system andsample chamber. The term “cytometry” technically refers to the countingor enumeration of cells, particularly blood cells. The term “cytometry”is used generically in this disclosure to refer to the enumeration ofany of a number of analytes, particularly particle analytes, describedin more detail below. The term “static” implies that the disclosedsystem and methods do not require that target analytes (for example,cells or particles) move or flow at the time of identification andenumeration. This in contrast to “flow cytometry,” a technical method inwhich target analytes (e.g., cells or particles) are identified and/orenumerated as they move past a detector or sets of detectors. Examplesof static cytometry include hemocytometers such as the Petroff-Hausercounting chamber, which is used with a conventional light microscope toenumerate cells in a sample. Cell staining apparatus and fluorescencemicroscopy instrumentation can be used to perform fluorescence-basedstatic cytometry. The present disclosure provides methods, devices, andinstruments for performing static cytometry analysis on a sample.

The methods and systems described herein generally relate to assays thatuse fluorescence signals to identify and/or enumerate analyte(s) presentin a sample. In exemplary applications, target analytes are specificallylabeled with fluorophore-conjugated molecules such as an antibody orantibodies (immunostaining). Other molecular recognition elements may beused, including but not limited to aptamers, affibodies, nucleic acids,molecular recognition elements, or biomimetic constructs. Non-specificfluorophores may also be used, including but not limited to stains suchas propidium iodide, membrane specific fluorophores, and fluorescentnuclear stains. Generally speaking, electronic images are formed of thefluorescence signals, wherein labeled analytes generate local maxima inthe electronic images. Image processing is later utilized to identifythe maxima and determine their correspondence to analytes or particlesof interest.

In exemplary embodiments, excitable tags are used as detection reagentsin assay protocols. Exemplary tags include, but are not limited to,fluorescent organic dyes such as fluorescein, rhodamine, and commercialderivatives such as Alexa dyes (Life Technologies) and DyLight products;fluorescent proteins such as R-phycoerythrin and commercial analogs suchas SureLight P3; luminescent lanthanide chelates; luminescentsemiconductor nanoparticles (e.g., quantum dots); phosphorescentmaterials, and microparticles (e.g., latex beads) that incorporate theseexcitable tags. For the purpose of this disclosure, the term“fluorophore” is used generically to describe all of the excitable tagslisted here. The terms “fluorophore-labeled,” “fluor-labeled,”“dye-labeled,” “dye-conjugated,” “tagged,” and “fluorescently tagged”may be used interchangeably in this disclosure.

The terms “color” and “color images” in this disclosure are intended asfollows. “Color” may refer to a specific wavelength or wavelength band.However, “color images” are intended as meaning grayscale images formedwhile a sample is illuminated under a specific color. Thus, “two colorimages” is to be interpreted as two grayscale images formed underillumination by different colors at separate times. Similarly, a “colorchannel” refers to operation of a system herein during illumination witha specific color. For example, “electronic images recorded in differentcolor channels” is to be interpreted as electronic images formed underillumination by different colors.

The embodiments described herein may be applicable to assays beyondfluorescence-based signal transduction. For example, the methods andsystems may also be compatible with luminescence, phosphorescence, andlight scattering based signal transduction.

In one embodiment, two color fluorescence microscopy based on laserillumination and differential immunostaining are used to identify andenumerate analytes in a sample. The present disclosure provides a methodand system for performing this analysis. In one example, differentialimmunostaining with anti-CD4 and anti-CD14 antibodies may be used toidentify CD4 T helper lymphocytes in blood. In another example,differential immunostaining with anti-CD4 and anti-CD3 antibodies areused to identify CD4 T helper lymphocytes in blood.

In another example, differential immunostaining with anti-CD4, anti-CD3,and anti-CD45 (three color system) are used to identify CD4 T helpercell percentage (% CD4) in a blood sample. In still another example,differential immunostaining with anti-CD4, anti-CD3, and anti-CD8antibodies is used to identify and enumerate both CD4 and CD8 Tlymphocytes such that the CD4/CD8 T lymphocyte ratio is obtained inaddition to the CD4 T helper lymphocyte count.

The terms “T cells” and “T lymphocytes” may be used interchangeably inthis disclosure. The terms “T helper cells,” “CD4 T helper cells” and“CD4 T cells” may be used interchangeably in this disclosure to refer tothose T helper cells that express CD4 on their surface.

For purposes of this disclosure, a cell that binds to a labelingmolecule with substantial affinity may be termed “positive” for thatparticular labeling molecule. Conversely, a cell that does not bind to alabeling molecule with substantial affinity may be termed “negative” forthat particular labeling molecule. For instance, a cell that binds ananti-CD4 antibody with a fluorescence tag and shows up as a detectablefluorescence event when illuminated may be termed “CD4 positive.”Conversely, a cell that does not show up as a detectable fluorescenceevent after incubation with an anti-CD4 antibody with a fluorescence tagunder the same or similar conditions may be termed “CD4 negative.”

Plural or singular forms of a noun may be used interchangeably unlessotherwise specified in the disclosure.

FIG. 1 illustrates a particle identification system 10 that isconfigured to receive and analyze a sample contained within a cartridge13. Cartridge 13 accepts a fluid sample as described herein, and loadsinto system 10, as illustrated. Particle identification system 10 isoperable to identify and/or count particles within the sample. Examplesof particles include analytes such as CD4+T-helper cells, other celltypes, bacteria, viruses, fungi, protozoa, and plant cells. System 10may also be operable to identify and/or count non-particle analytes suchas proteins, peptides, prions, antibodies, micro RNAs, nucleic acids,and sugars.

FIG. 2 shows a schematic cross sectional view of one particleidentification system 100. Particle identification system 100 is anexample of particle identification system 10, FIG. 1. Elements of system100 include:

-   -   an enclosure 110 that provides mechanical support and optical        isolation for system 100;    -   a cartridge handling system 120 shown with a cartridge 130 in a        measurement position; cartridge 130 containing the sample under        test;    -   imaging optics 140 including an emission filter 150, a focus        adjusting system that adjusts focus of imaging optics with        respect to cartridge 130 is not labeled in FIG. 2 (see FIG. 11);    -   a sensor 160 that provides electronic images of a measurement        field (MF) 135 of cartridge 130 that is imaged through imaging        optics 140 and emission filter 150, imaging optics 140 and        sensor 160 are sometimes referred to collectively herein as an        imager;    -   a first illumination module 200 emitting first electromagnetic        radiation 210;    -   a beam expander 220 and an aperture 230 for defining a first        illumination beam 240 from first electromagnetic radiation 210;    -   a first folding mirror 250 that reflects beam 240 so that beam        240 intersects cartridge 130 at measurement field 135;    -   a rotating phase plate 245 through which beam 240 passes;    -   a second illumination module 300 that emits second        electromagnetic radiation as a second illumination beam 310;    -   an excitation filter 320;    -   a second folding mirror 330 that reflects beam 310 so that beam        310 also intersects cartridge 130 at measurement field 135; and    -   a controller 450. FIG. 2 shows controller 450 within enclosure        110, and controller 450 may be provided within enclosure 110 but        may, alternatively, be provided externally to enclosure 110        (e.g., through electrical and/or wireless connections to a        computer or network). Controller 450 is described in greater        detail in connection with FIG. 11.

System 100 works by sequentially illuminating a stained sample withincartridge 130 to cause fluorescence of particles within the sample,capturing images of the fluorescence, and analyzing the images toidentify particles in the sample and to determine the presence ofbiological markers therein. The illumination is by electromagneticradiation which is typically visible light, but radiation of other types(e.g., infrared, ultraviolet) may also be utilized by adapting themodalities described herein. The systems and methods described hereinprovide a user interface and robust clinical capabilities by identifyingand counting analytes in even unfiltered whole blood samples, althoughthey can also work with lysed, diluted, and/or filtered samples. Detailsof system 100, cartridge 130 and associated methods and software to doso are now provided. It should be clear that cartridge 130 is compatiblewith system 100, but it is appreciated that cartridge 130 may also beusable in other readers. Likewise, other cartridges could be usable insystem 100.

As shown in FIG. 2, the optical paths of illumination beams 240 and 310are separate, permitting separate optimization thereof. Given thetypical sense of “copropagating” in optics as meaning that two lightbeams share an optical path (as is often the case in microscopes andflow cytometers), beams 240 and 310 are nowhere copropagating.Illumination beam 240 may be nominally green light (e.g., having a peakwavelength of about 510 to 550 nm), which is strongly absorbed by redblood cells. Use of light in this wavelength range can lead to localizedheating of a blood sample, causing motion of cells in the sample thatmay complicate measurements. The optical path of illumination beam 240can therefore include an aperture that limits the size of, and thus theoptical power transmitted by, beam 240 toward cartridge 130.Illumination beam 310 may be nominally red light (e.g., having a peakwavelength of about 625 to 670 nm). Illumination modules 200 and 300 maybe any type of electromagnetic radiation sources such as, withoutlimitation, solid state lasers, gas lasers, fiber lasers, LEDs,superluminous LEDs or filtered incandescent or fluorescent lightsources.

Illumination beams 240 and 310 may advantageously be arranged such thattheir incidence on, and reflections from, cartridge 130 are at anglesthat fall outside a numerical aperture of imaging optics 140. This helpsimprove signal to noise ratio of images captured by sensor 160. AlthoughFIG. 2 shows illumination beams 240 and 310 impinging on MF 135 from oneside of cartridge 130 and imaging optics 140 being on the other side ofcartridge 130, it is contemplated that one or both (or more)illumination beams may be on the same side of cartridge 130 as imagingoptics 140. In FIGS. 2-4, illumination beams 240 and 310 are shown asbeing coplanar (that is, each of beams 240 and 310 lies in the plane ofthe cross-section). Alternatively, in embodiments, illumination beamsmay be reconfigured so as not to be coplanar. It is also appreciated byone skilled in optics that similar systems could be implemented withoutor with fewer or more folding mirrors than are shown in system 100, FIG.2. Also, additional illumination modules and optical paths may beincluded to form, e.g., a three-wavelength or four-wavelength system.

Phase plate 245, through which beam 240 passes, has a characteristicfeature size and a rotation rate to decohere laser light such that laserspeckle effects or other interference-induced illumination nonuniformityin beam 240 are averaged out over the duration of a measurement. Asshown, system 100 includes phase plate 245 only in the path of beam 240,but it is contemplated beam 310 could pass through an identical orsimilar phase plate if similar effects are expected in beam 310.

FIG. 3 shows an enlarged, schematic cross sectional view of a portion ofthe system of FIG. 2, with certain structure removed for clarity ofillustration. Illumination beams 240 and 310 are directed towardsmeasurement field 135, where cartridge 130 holds a sample within adetection region 137 (detection region 137 is an example of detectionregion 2700; see FIGS. 47, 48). Detection region 137 is depicted as aheavy line and is not to scale. Imaging optics 140 image measurementfield 135 onto sensor 160, which generates electronic images ofmeasurement field 135 that are further processed to identify analytes inthe sample. In the context of the present document, a “measurementfield” is therefore a portion of a sample within detection region 137,and corresponds to a field of view of imaging optics 140 throughdetection region 137, or a portion of such field of view as imaged bysensor 160, as described below. Imaging optics 140 include an emissionfilter 150 that filters out light that is not within the wavelengthrange(s) corresponding to fluorescence emitted by the analytes, andlight that is in the wavelength range(s) corresponding to illumination240 and 310 when incident on cartridge 130, thus improving signal tonoise ratio of the electronic images. Although emission filter 150 isshown within optics 140 in FIGS. 2 and 3, other embodiments may includeemission filter at other locations, or may omit emission filter 150completely. FIG. 3 also shows an admittance cone 142 of imaging optics140 that is defined by the numerical aperture of optics 140. Althoughthe angles of admittance cone 142 and illumination beams 240 and 310 maynot be to scale in FIG. 3, the numerical aperture of optics 140 andtherefore the angle of admittance cone 142 are chosen such thatillumination beams 240 and 310 will not enter imaging optics 140. It isappreciated that this choice helps keep illumination beams 240 and 310themselves, or partial reflections thereof propagated at similar anglesthrough cartridge 130, from entering imaging optics 140 where they coulddegrade the signal to noise ratio of the system.

Emission filter 150 may be a dual-bandpass filter with one bandpass setto transmit at least a portion of the fluorescence emission producedwhen illuminating with illumination beam 240, and the other bandpass setto transmit at least a portion of the fluorescence emission producedwhen illuminating with illumination beam 310, while blocking light atthe wavelengths of illumination beams 240 and 310. If more illuminationsare added, the number of bandpasses in emission filter 150 may beincreased (e.g., three lasers and a triple-bandpass filter). In oneembodiment, multiple single-bandpass emission filters are placed in afilter-changing mechanism that is motorized and controlled by a controlsystem. In yet another embodiment, fluorophores are chosen to share anemission wavelength range and have significantly different excitationspectra, such that they are selectively excited by individualillumination beams but detected using a single emission filter having asingle bandpass. In a further embodiment, fluorophores are chosen toshare an excitation wavelength range and have significantly differentemission spectra, such that all fluorophores are excited by the sameillumination while a filter-changing mechanism with multiplesingle-bandpass filters is used to selectively detect emission fromdifferent fluorophores.

FIG. 4 schematically shows a region immediately surrounding measurementfield 135, which is depicted as a heavy line that is not to scale.Illumination beams 240 and 310 intersect, and are refracted by,cartridge 130 to form an illuminated region of detection region 137 thathas a width 350, as shown. Fluorescently tagged analytes withindetection region 137, if present, may emit fluorescence in alldirections; only fluorescent rays 360 propagating in the direction ofimaging optics 140 are depicted in FIG. 4. Imaging optics 140 mayinclude an aperture stop 145 that is shown schematically in FIG. 4; oneskilled in the art will appreciate that the location and geometry ofaperture stop 145 within optics 140 may be different from the exactlocation and geometry shown in FIG. 4. In particular, aperture stop 145may be located on a surface of optics 140 that faces cartridge 130. Ifaperture stop 145 is located within optics 140 at a position where theimage of measurement field 135 is not at 1:1 magnification, thenaperture stop 145 may be sized differently than shown in FIG. 4.Aperture stop 145 stops fluorescent rays 360 except for rays 380 thatemanate from a width 370 within detection region 137. Rays 380 continueto sensor 160. Width 370 therefore laterally defines measurement field135.

In alternative embodiments, imaging optics 140 do not include aperturestop 145, but may instead create an image of rays 360 that exceeds asize of sensor 160 at a focal plane of the optics, in which case thesize of sensor 160 laterally defines width 370 and measurement field135. In another embodiment, imaging optics 140 may include a field stop(not shown in FIG. 4) to increase the depth of field.

Several aspects of cartridge 130 are advantageously arranged to improvesensitivity of system 100 to particles bearing biological markers. Inone embodiment, cartridge 130 is fabricated of an optical grade, clearmaterial to enable distortion free and loss free imaging of the sampletherethrough. The material may be a low autofluorescence plastic such ascyclic olefin polymer, cyclic olefin copolymer, polystyrene,polymethylmethacrylate, polycarbonate, etc. to avoid generating straybackground light, from which fluorescence of sample particles would haveto be distinguished. A precisely known height of a fluidic channelwithin cartridge 130, including each of the MFs to be measured, may be acritical dimension. If a field of view of optics 140 determines atwo-dimensional area of a measurement field of the sample beingmeasured, the channel height times the area will determine the volume,such that knowing the height precisely limits the measurement accuracyof particle concentration by volume. Filling of the channel from floorto ceiling (e.g., in the dimension parallel to the optical axis of theimaging system) can be achieved through an appropriate combination ofchannel height and surface energy. The surface energy can be increasedby, e.g., plasma cleaning and/or chemical surface modification.Cartridge 130 may be configured with an advantageously small channelheight to aid filling. A small channel height dimension further reducesthe absorption of excitation illumination and fluorescence emission bysample components such as red blood cells.

A wide viewing angle in optics 140 limits a depth of field of theimaging system formed by imaging optics 140 and sensor 160. It isadvantageous to count all particles within a MF in a single image,rather than acquiring several images at varying focus depths within asample and sorting unique from non-unique particles within the images.For this reason, it may be desirable to match the depth of field of theimaging system to the channel height. That is, the imager having a depthof field that is commensurate with channel height may be regarded as thechannel height being within ±20% of the depth of field of the imageralong a viewing axis of the imager. Alternatively, if depth occupied byfluorescently labeled particles within the cartridge is known, it may bedesirable to match the depth of field of an imager to such depth. Thatis, the imager having a depth of field that is commensurate with depthoccupied by fluorescently labeled particles within the cartridge may beregarded as that depth being within ±20% of the depth of field of theimager along a viewing axis of the imager. It may also be desirable tomake portions of cartridge 130 adjacent to the MF much thicker than thedepth of field (so that stray material such as dust and fingerprintsoutside the sample chamber is substantially out of focus, minimizing thechances that such material will distort images or be mistaken for atarget analyte). This is illustrated further in FIG. 5.

FIG. 5 shows details of structures within, and vertically surrounding, aportion of measurement field 135 within cartridge 130. Measurement field135 is shown with a sample 8 therein. When illuminated by illuminationbeams 240 and/or 310 (see FIGS. 2 through 4), fluorescently labeledanalytes designated as T emit fluorescent rays. Although onlyfluorescent rays 360 propagating in the direction of the imaging optics140 are shown in FIG. 4, it is understood that fluorescently labeledanalytes T may emit in all directions. As discussed above, imagingoptics 140 and/or sensor 160 laterally define measurement field 135outside of the portion shown in FIG. 5. Detection region 137 withincartridge 130 has a channel height 385, which as discussed above isanother dimension needed to define a volume of sample 8. Imaging optics140 (see FIGS. 2 through 4) have a depth of field 390 centered about afocal plane 392 within detection region 137. In the embodiment shown inFIG. 5, depth of field 390 is advantageously about the same as, orslightly greater than, channel height 385. Cartridge 130 includes upperand lower elements 131, 132 that bound detection region 137 and, to theextent that these elements are optically transmissive, they are muchthicker than depth of field 390, to keep nuisance artifacts outsidecartridge 130 out of focus. For example, either or both of upper andlower elements 131, 132 may be an optically transmissive, planarsubstrate that is at least three times thicker than a depth of field ofimaging optics 140 along a viewing axis of the imager. In otherembodiments, channel height 385 may be greater than depth of field 390;in these embodiments imaging may be coordinated with focus adjustmentsof imaging optics 140 to provide multiple measurement fields separatedby height within cartridge 130.

When a measurement of analyte concentration within a volume is based ona number of analytes detected within a two-dimensional projection of thevolume, the accuracy of the measurement is limited by the accuracy towhich the third dimension, eliminated in the projection, is known. Thissituation is encountered, for example, when the number of analytes in avolume is determined by two-dimensional imaging of the volume, like thesituation presented in FIGS. 2-5. In cases where uncertainty in thethird dimension is the dominant contributor to the uncertainty of theanalyte concentration, the relative uncertainty of the analyteconcentration equals the relative uncertainty of the extent of the thirddimension. This is critically important when a particle identificationsystem is designed to operate on microliter or picoliter quantities ofbiological samples (e.g., one or two drops of blood) because the channelheight that defines the third dimension may be on the order of tens ofmicrons, and such heights are difficult to provide with high precision(e.g., with tolerances of less than around 10%). Surfaces may be eitherphysical (e.g., defined by physical materials) or defined by aspects ofthe detection. Examples of physical surfaces include substrates,membranes, and material discontinuities. Examples of non-physicalsurfaces include detection aspects such as the depth of field of animaging system or endpoints of a scan along the direction of projection.

The average analyte concentration n in a volume V is given by n=N/V,where N is the number of analytes within the volume. The volume V is thelocal volume height, integrated over the projected area, A, included inthe measurement. This integration reduces to the area-weighted averageof the local height, h_(average). With these definitions, the volume Vcan be written as V=A×h_(average). Consequently, the analyteconcentration is given by n=N/(A×h_(average)). This equation underlinesthe importance of an accurate determination of the volume height. Actualknowledge of the local volume height, h_(local), is not required. It issufficient to determine the h_(average), i.e., the area-weighted averageof the local height.

Channel height 385 (FIG. 5) is often determined by parameters that arenot intrinsic to the detection system (the cartridge-reading instrumentonly, excluding the cartridge) for instance a distance between upper andlower elements 131, 132 in cartridge 130. FIG. 6 schematically shows aportion of a cartridge 130(1), formed of upper and lower elements 131,132. Cartridge 130(1) exhibits variation of channel height 385 acrossmeasurement fields 135(1) through 135(8) (represented by dashed linescrossing detection region 137, since measurement fields 135 are definedas areas imaged by optics of a reader, as per the discussion above, inconnection with FIG. 4). Certain features in FIG. 6 are exaggerated forillustrative purposes. A portion of FIG. 6 designated as A is shown indetail in FIG. 7, indicating haverage within measurement field 135(5) ofcartridge 130(1).

In one embodiment, measurement of analytes may be performed togetherwith a measurement of haverage for each measurement field. In anotherembodiment, channel height 385 may be mapped out and recorded in advanceof the analyte measurement and applied in the calculation of the deducedanalyte concentration. For instance, a channel height measurement may beperformed during production of cartridge 130. In an embodiment, acharacterization of channel height 385, in the form of, e.g., a singlehaverage or a map consisting of a series of haverage values, may beencoded on cartridge 130 and read either by an operator or by aninstrument. For instance, a barcode or other machine-readableinformation that contains channel height information may be labeled on acartridge 130, and the barcode may be read by a barcode reader at thetime of analyte measurement. The barcode reader may be integrated in theinstrument performing the analyte measurement (e.g., system 100), it maybe connected to the instrument, or it may be separate from theinstrument.

A channel height characterization for individual cartridge 130 may beintegrated in the cartridge production process. The characterization maybe performed on all cartridges or it may be performed on a subset ofdevices, for instance a suitable number of cartridge 130 may beextracted from each production run or each lot of cartridges provided toa customer. Techniques for characterizing channel height include but arenot limited to white light interferometry in transmission or reflectionmode. Ideally, for preservation of materials, the measurement isnon-destructive. That is, the cartridges exposed to the measurement arestill usable for analyte concentration measurements. Opticalinterrogation methods are ideal for this purpose as long as the relevantsurfaces of the cartridges can be accessed optically. In the case ofanalyte detection systems based on imaging or other optical detectionschemes, an optical path through the cartridge that is used by thedetection system can be used for characterizing the channel height.Other access paths, if available, may also be used.

FIG. 8 shows a schematic cross sectional view of one particleidentification system 100′. Particle identification system 100′ is anexample of particle identification system 10, FIG. 1. Elements of system100′ include:

-   -   an enclosure 110′ that provides mechanical support and optical        isolation for system 100′;    -   a cartridge handling system 120′ shown with a cartridge 130′ in        a measurement position; cartridge 130′ containing the sample        under test;    -   imaging optics 140′ including an emission filter 150′, a focus        adjusting system that adjusts focus of imaging optics with        respect to cartridge 130′ is not labeled in FIG. 8 (see FIG.        11);    -   a sensor 160′ that provides electronic images of a measurement        field (MF) of cartridge 130′ that is imaged through imaging        optics 140′ and emission filter 150′ imaging optics 140′ and        sensor 160′ are sometimes referred to collectively herein as an        imager;    -   an illumination subassembly 205 that includes first and second        illumination modules (not shown in the cross-sectional plane of        FIG. 8) emitting first and second illumination along a common        beam path 215 that intersects cartridge 130′ at measurement        field 135′;    -   a rotating phase plate 245′ through which beam path 215 passes;        and    -   a controller 450′. FIG. 8 shows controller 450′ within enclosure        110′, and controller 450′ may be provided within enclosure 110′        but may, alternatively, be provided externally to enclosure 110′        (e.g., through electrical and/or wireless connections to a        computer or network). Controller 450′ is described in greater        detail in connection with FIG. 11.

Cartridge handling system 120′ accepts cartridge 130′ from an operatorthat loads cartridge 130′ into a slot (not shown) in a front panel ofenclosure 110′. Thereafter, cartridge handling system 120′ movescartridge 130′ into place for imaging by sensor 160′ through imagingoptics 140′, including repositioning cartridge 130′ for imaging ofspecific measurement fields therein. As opposed to the arrangement ofparticle identification system 100, FIG. 2, particle identificationsystem 100′ is configured for same side imaging, that is, beam path 215impinges on cartridge 135′ from the same side as the optics used toimage measurement fields within cartridge 135′. Consequently, opticalaccess to the sample is required from one side only, and cartridgematerials on the opposite side of the detection region from theillumination and the optics need not be transparent. This has multiplebenefits. For instance, labels can be applied to the side of thecartridge that does not face the illumination and optics. Also, the sameside of the cartridge can be formed of opaque and/or light absorbingmaterial that is well-suited for laser welding. The same side of thecartridge may also be of any color, for easy identification, andrequires no particular optical performance or features.

Illumination subassembly 205 utilizes a dichroic beam-combiner tocombine two illumination beams (e.g., of different wavelength bands, forstimulating different fluorescent labels) prior to the beams beingdirected along beam path 215 toward cartridge 135′. This allows forcomplete assembly and alignment of illumination subassembly 205 beforeinstallation into system 110′, as well as minimizing a number of opticalpaths into the cartridge area.

FIG. 9 is a schematic block diagram of a particle identification system100″. Particle identification system 100″ is an example of particleidentification systems 10, 100, 100′. System 100 includes a cartridge130″ that contains a sample with fluorescently labeled particles. Aregion within cartridge 130″ is illuminated by illumination 200″; atleast one measurement field of cartridge 130″ is imaged by imager 141that provides wavelength-filtered electronic images of the measurementfields to a particle identifier 401, as shown. Particle identifier 401processes the electronic images to determine a superset of particles ofinterest, and determines fluorescently labeled particles within thesuperset based on properties of the fluorescently labeled particles inthe at least one measurement field.

FIG. 10 is a flowchart of a method 400 for determining fluorescentlylabeled particles within a sample. Step 410 processes at least oneelectronic image from at least one focal position within the sample.Step 420 determines dimmest separation lines between brighter areas inthe electronic image. Step 430, for each of the brighter areas,determines local background level based on pixel values of theseparation lines forming a perimeter therearound, to determine each ofthe fluorescently labeled particles. Examples and details of steps 410through 430 are provided below in connection with FIGS. 11 through 30.

FIG. 11 is a schematic block diagram illustrating functionalrelationships between certain components of systems 100, 100′ and/or100″ (labeled collectively in FIG. 11 as 100) and illustrating featuresof controllers 450 thereof (the components shown in FIG. 8 arereferenced by their corresponding numbers in FIG. 2 for simplicity inthe discussion of FIG. 11). Cartridge 130 is positioned by cartridgehandling system 120 with respect to imaging optics 140. Illuminationmodules 200, 300 provide illumination for cartridge 130. Imaging optics140 are focused by focus adjusting system 147 as discussed furtherbelow, to adjust focus of cartridge 130 on sensor 160. As noted above,controller 450 may be integrated within enclosure 110 of systems 100,100′, or may be provided externally to enclosure 110 through electricalor wireless connections. Connections between controller 450 and othercomponents of systems 100, 100′ that provide information transfer, imagetransfer or control are shown in solid lines, while opticalrelationships among some of the components are shown as broken lines.Connections within controller 450 are not shown, for clarity ofillustration.

Controller 450 includes a processor 460 that is typically amicroprocessor or microcontroller, but could be implemented in otherknown ways (e.g., with discrete logic, ASIC or FPGA semiconductors, orother electronic hardware with equivalent functionality). Controller 450also includes memory 470 for storing software, filter kernels, images,calculations and results thereof. FIG. 11 shows memory 470 storingfilter kernels K and/or K′, and exemplary software instructions formethods including main routine 800, auto focus 900, calculate focusposition 1000, calculate focus metric 1100, flat field mask 1200,process source image 1300, prefilter 1400, remove background 1500,calculate watershed lines 1600, morphological reconstruction 1650,declump 1700, morphological intersection 1800, find blobs 1900, filterblobs 2000, correlate source images 2100, compensate chromaticaberration 2200 and filter low intensity correlations 2300. Kernels Kand/or K′, and the software instructions illustrated in FIG. 11 aredescribed in greater detail below, in connection with FIGS. 13-30. Uponexecuting some or all of the software noted above, controller 450functions as particle identifier 401, FIG. 9.

FIG. 12 is a flowchart of a method 500 of counting particles in asample. Steps 550 through 590 are steps that may be performed entirelyby particle identification systems described herein; steps 510 through530 may be performed utilizing some of the system described herein orwith other tools, while steps 580 through 590 are image processing andstatistical analysis steps that may be performed by certain embodimentsbut could also be performed utilizing a computer, calculator or thelike, or not at all. Certain steps of method 500 shown in FIG. 12 arehigh-level descriptions of exemplary procedures that will be describedin greater detail below. It is appreciated that some of the proceduresdescribed further below are optional, but may increase precision ofparticle counts. Also, method 500 is described in the context of bothphysical, data-taking steps (e.g., the steps wherein a sample isobtained, processed and imaged) and image processing that results inparticle counts, but the image processing steps can also be performed onimages that are stored or obtained by other means than the physicaldata-taking steps. Where the following discussion pertains to specificcomponents noted in FIG. 2, it also pertains to the same-namedcomponents noted in FIG. 8 (e.g., sensor 160 pertains to sensor 160′,controller 450 pertains to controller 450′ etc.).

Step 510 obtains a whole blood sample from a patient. In embodiments, acartridge (e.g., cartridge 13 or 130, FIGS. 1-5) includes an inlet portthat can be touched directly to a capillary whole blood droplet (e.g.,from a finger stick). Such cartridge may also include features thatpromote capillary action to draw the blood droplet into the cartridge(see also FIGS. 45 through 50). Alternatively, the capillary bloodsample may be collected via an uncalibrated or calibrated transferpipette. In another embodiment, the sample is venous whole bloodcollected in a blood tube (e.g., BD Vacutainer®). Step 520 adds thewhole blood sample to one or more fluorescently stained reagent(s) toform a stained sample. In embodiments, the fluorescently stainedreagent(s) may be provided in the cartridge; alternatively, thereagent(s) may be combined externally to the cartridge (for example, ina microtube) to form the stained sample, which is then loaded into thecartridge. An optional step 530 incubates the stained sample to providetime for the reagent(s) to mix and/or react with the whole blood sample.Step 530 may also be performed while the stained sample is outside thecartridge, and/or in the cartridge. Also, step 530 may be performedpartly while the sample is in the detection region. Step 540 loads thestained sample into a detection region of the cartridge. In the case ofa cartridge supplied with reagents therein, step 540 is the same assteps 510 and 520, that is, the cartridge itself obtains the whole bloodsample and draws the sample into the detection region. Reagents may belocated in a reagent region upstream from the detection region and/or inthe detection region. In other embodiments, the sample and reagents aremixed outside the cartridge, and are loaded into the cartridge(including a detection region thereof) in step 540.

Step 550 loads the cartridge into a reader (e.g., systems 100, 100′,FIG. 2 and FIG. 8). Step 560 moves the cartridge to a measurement fieldto be counted, that is, the reader operates a mechanism that moves thecartridge to a location such that illumination sources and imagingoptics can cooperate to perform steps 570 and 575. Step 560 may beperformed, for example, as part of main routine 800, described later inconnection with FIG. 13. There may be only one, or many, suchmeasurement fields within a cartridge; having multiple measurementfields provides improved statistical accuracy for particle counts. Step570 illuminates a measurement field with a first excitation light,filters emitted light from the sample, and forms a first image of themeasurement field. Step 570 may be performed, for example, as part ofmain routine 800, described later in connection with FIG. 13. An exampleof step 570 is utilizing first illumination module 200 and associatedoptics (see, e.g., FIG. 2) to form illumination beam 240, illuminatingthe measurement field with beam 240, passing fluorescence from stainedparticles within the sample through imaging optics 140 and emissionfilter 150, and acquiring an image of the measurement field with sensor160. The image acquired in step 570 may be sufficient for some purposes.An optional step 575 illuminates the same measurement field with asecond excitation light, filters emitted light from the sample, andforms a second image of the measurement field. Step 575 may also beperformed, for example, as part of main routine 800, described later inconnection with FIG. 13. An example of step 575 is utilizing secondillumination module 300 and associated optics (see, e.g., FIG. 2) toform illumination beam 310, illuminating the measurement field with beam310, passing fluorescence from stained particles within the samplethrough imaging optics 140 and emission filter 150, and acquiring animage of the measurement field with sensor 160.

Step 580 analyzes at least the first, and optionally the second image(s)to count fluorescent particles in each image. Step 580 may includeexecution of the software instructions illustrated in FIGS. 16, 17,19-27 and 30, and may include convolution of images with kernels K or K′illustrated in FIG. 18. Step 580 may be performed as soon as steps 570and 575 are complete; alternatively, the first and second imagesgenerated in steps 570 and 575 may be stored for later analysis in steps580. That is to say, images stored at any time may be analyzed in step580 independently from the acquisition of the images; in fact, imagesanalyzed in step 580 may or may not have been acquired exactly as shownin steps 510 through 575. As part of step 580, an optional step 585correlates the first and second image to find particles that arefluorescent in both images. Step 585 may include execution of thesoftware instructions illustrated in FIGS. 28-30.

Although not shown in FIG. 12, automatic or manual focusing, or focusadjustments (e.g., based on measurements from a focus routine) may beperformed before or in between any of the illuminating and imaging stepsof method 500. For example, an autofocus routine may be performed byexecuting the software instructions illustrated in FIGS. 14-17, whichmay include convolution of images with kernel K illustrated in FIG. 18.

After step 575 or 580, method 500 optionally reverts to step 560 so thatthe cartridge moves to another measurement field to be counted, andsteps 570 through 575 (and optionally step 580) are repeated. In analternative embodiment, steps 560 and 570 may be performed for allfields of view prior to steps 560 and 575 being performed for all fieldsof view. If multiple fields of view are measured, when all such fieldsof view have been measured, an optional step 590 generates statisticsfrom the particle counts generated in step 590.

II. Particle Counting Methods and Software

FIG. 13 is a flowchart of an exemplary method for gathering andprocessing data in a particle identification system. The illustratedmethod is called herein main routine 800, and may be performed bysystems 100, 100′ to provide a particle count for a sample within acartridge (e.g., cartridges 130, 130′, FIGS. 2 and 8, or cartridges2600, 2600′, 2600″, FIGS. 45-50). “Routine” is used here in the sense ofa computer program or subprogram (e.g., subroutine). In an embodiment,main routine 800 requires no data input and provides one or moreparticle counts or other measurements as output. In another embodiment,main routine 800 receives input data in the form of parameters relatedto the cartridge being utilized or what kinds of particles or otherevents are to be counted. Such input data may be in the form ofinformation read from indicia located on the cartridge (e.g., as abarcode or 2D barcode) or may be manually entered into systems 100,100′. The steps of main routine 800, and the subroutines performedtherein, will be described in roughly the order that they are typicallyused; however, in embodiments certain steps may be performed in adifferent order or not at all. That is, no particular step of mainroutine 800 or the methods further detailed below is consideredindispensable, some of these steps may be omitted for cost or timesavings, possibly resulting in less accurate particle counts).

One exemplary feature of main routine 800 is that care is taken toestablish precise focus of imaging optics 140 on measurement fields ofcartridges 130, 130′ for particle measurement by generating focusmetrics related to the actual particles of a given sample, rather thanby focusing on artifacts in the sample or on the cartridge. Thereforecertain image processing steps will be initially discussed in relationto their support of autofocus routines, but as seen later the same stepswill also be utilized for image processing for the particle counting. Itshould also be noted that various routines called by main routine 800first identify “blobs” within images of the sample, then apply screensto the blobs to distinguish those blobs that likely represent particlesof interest from those that do not. In this context, “blobs” are areasof local brightness within an image. The screens disclosed herein aredescribed in order to enable one of ordinary skill in the related art tomake and/or use particle identification systems, but not every screenmentioned is critical; certain of the screens may be performed in anorder different from that specified here, or omitted, while otherscreens may be added. Generally speaking, the routines disclosed hereinidentify blobs or other events within at least one image of ameasurement field that can be considered a superset of particles orevents of interest, and determine fluorescently labeled particles orother events within the superset based on properties of the particles orevents in the measurement field.

Step 805 of main routine 800 receives a cartridge into a system. Anexample of step 805 is systems 100, 100′ receiving cartridges 130, 130′,FIGS. 2, 8. Step 810 of main routine 800 runs an autofocus routine toestablish appropriate focus adjustment of imaging optics on thecartridge. An example of step 810 is utilizing autofocus 900, FIG. 14,and its called subroutines to establish appropriate setting of focusmechanism 147 such that one or more measurement fields of cartridges130, 130′ are focused by imaging optics 140 on sensor 160. Step 820 ofmain routine 800 moves the cartridge to a measurement field to becounted. Step 830 sets focus of the imaging optics based on results ofthe autofocus routine. An example of step 830 is utilizing the resultsof autofocus 900 to control focus mechanism 147 to focus an image ofcartridges 130, 130′ on sensor 160 for the particular measurement fieldto be counted.

Step 840 of main routine 800 enables an illumination module, acquires anelectronic image S, and disables the illumination module. A firstexample of step 840 is turning on illumination module 200 of systems100, 100′, acquiring an image S of a measurement field within cartridges130, 130′ from sensor 160 while illumination module 200 is on, thenturning illumination module 200 off. Step 845 processes image S toidentify and perform preliminary filtering on “blobs” identified withinimage S. As used herein, “blobs” are local areas of high intensitypixels within an electronic image. Such areas may or may not correspondto particles to be counted, many of the steps described in connectionwith FIGS. 19-30 are designed to help discriminate blobs that should becounted as particles of interest from those that should not. An exampleof step 845 is controller 450 performing process source image 1300, FIG.19 (including its called subroutines). Step 845 involves only dataprocessing as opposed to hardware manipulation, therefore step 845 maybe performed within the sequence of main routine 800 as illustrated, orat any time after step 840 is performed. For example, main routine 800may be repeatedly executed to generate multiple images S that can besaved in memory 470 for later processing. Also, images S can betransmitted from systems 100, 100′ to a remote computer for processing(e.g., the remote computer is considered to form part of controller450).

Step 850 makes a decision according to the number of illuminationmodules to be utilized for counting particles. If another image S andits associated processing are required, main routine 800 returns to step840 to acquire another image S (and optionally process the image S instep 845). Accordingly, another example of step 840 is turning onillumination module 300, acquiring an image S while illumination module300 is on, and turning illumination module 300 off. If images Sassociated with all appropriate illumination modules have been acquired,main routine 800 advances from step 850 to step 860.

Step 860 correlates images S that have been acquired using differentillumination sources. An example of step 860 is performing correlatesource images 2100, FIG. 28 (including its called subroutines). Step 870compensates for chromatic aberration and other sources ofmisregistration of two or more images. An example of step 870 isperforming compensate chromatic aberration 2200, FIGS. 29A-29B.

Step 880 makes a decision according to whether further measurementfields are to be measured. If so, main routine 800 returns to step 820.If not, main routine 800 proceeds to step 890.

Step 890 filters, based on intensity correlations out of the data takenin previous steps, if necessary, based on the data itself. Inembodiments herein, it may be advantageous to combine data for multiplemeasurement fields before step 890 is performed, so that the data isstatistically well behaved. However, in embodiments wherein the numberof events found per measurement field is high, step 890 could beperformed on data from a single measurement field, or on separate datasets from separate measurement fields before merging the data. Thiscould be advantageous for cases where particle brightness changessignificantly from one measurement field to the next, for example due toillumination intensity drift or fluorescence staining variation from onepart of a sample to another.

An example of step 890 is performing filter low intensity correlations2300, FIGS. 30A-30B. A final step 895 of main routine 800 returns one ormore particle counts. One example of step 895 is returning a singleparticle count from a single measurement field. Another example of step895 is returning a set of particle counts from multiple measurementfields, and/or statistics derived therefrom.

FIG. 14 is a flowchart of one exemplary method called autofocus 900, foroptimizing optical focus of a particle identification system on acartridge. The illustrated method is called herein autofocus 900, andmay be performed by systems 100, 100′ to provide an optimal focusposition for optics 140 relative to one or more measurement fieldswithin a cartridge (e.g., cartridges 130, 130′, FIGS. 2 and 8, orcartridges 2600, 2600′, 2600″, FIGS. 45-50). In an embodiment, autofocus900 requires no data input and provides at least an optimal focusposition for one measurement field within a cartridge as output. Inanother embodiment, autofocus 900 provides a function that identifiesoptimal focus position across multiple measurement fields of thecartridge as output. The function may for example be a linear rampfunction that interpolates optimal focus settings between first and lastmeasurement fields within the cartridge. Step 910 of autofocus 900 movesthe cartridge to the first measurement field (herein, moving a cartridge“to a measurement field” should be understood to mean that themeasurement field on the cartridge is positioned where imaging opticscan image the measurement field). An example of step 910 is controller450, 450′ of systems 100, 100′ (FIGS. 2, 6) controlling cartridgehandling system 120 or 120′ to move cartridge 130 or 130′ to a firstmeasurement field.

Step 920 of autofocus 900 runs a calculate autofocus position routine.An example of step 920 is running calculate autofocus position 1000,described below in connection with FIG. 15. Calculate autofocus position1000 returns an optimal focus setting for at least one illuminationmodule; it may also return an optimal focus setting for otherillumination modules by adding offset(s) to the first optimal focussetting.

Steps 930 through 960 of autofocus 900 are optional. If performed, steps930 through 960 provide measurements and calculate a function thatprovides optimal focus positions for multiple measurement fields on acartridge. Step 930 moves the cartridge to a last measurement field. Anexample of step 930 is controller 450, 450′ of systems 100, 100′ (FIGS.2, 6) controlling cartridge handling system 120 or 120′ to movecartridges 130, 130′ to a last measurement field. Step 940 runs thecalculate autofocus position routine again. An example of step 940 isrunning calculate autofocus position 1000 again. Optional step 950returns the cartridge to the first measurement field. An example of step950 is controller 450, 450′ of systems 100, 100′ (FIGS. 2, 6)controlling cartridge handling system 120 or 120′ to move a cartridge130 or 130′ back to the first measurement field. If steps 930 and 940were performed, an optional step 960 calculates a function that providesoptimal focus position for multiple measurement fields. An example ofstep 960 is calculating a linear ramp function that interpolates betweenoptimal focus positions of the first and last measurement fields, toprovide an optimal focus position for measurement fields that arebetween the first and last measurement field. Step 965 of autofocus 900returns either a single optimal focus position, or a function thatprovides optimal focus positions for a plurality of measurement fields.

It should be understood that more measurement fields may be measured byadapting step 930 to move a cartridge to such measurement fields ratherthan a last measurement field, and that step 940 may be repeated. Doingso can provide information that allows optional step 960 to calculatefunctions for optimal focus that may be more accurate for intermediatefields than the linear ramp function discussed above.

FIG. 15 is a flowchart of a method called calculate autofocus position1000 for generating an optical focus position of a particleidentification system on a single measurement field of a cartridge.Calculate autofocus position 1000 may, for example, be performed bysystems 100, 100′ to provide an optimal focus position for optics 140 or140′ relative to one measurement field of a sample within a cartridge(e.g., cartridges 130, 130′, FIGS. 1-5, or cartridges 2600, 2600′,2600″, FIGS. 45-50). In an embodiment, calculate autofocus position 1000provides an optimal focus position for one measurement field within acartridge, for one illumination module, as output. In anotherembodiment, calculate autofocus position 1000 also provides optimalfocus position (s) for the same measurement field, but for second orfurther illumination module(s), as output. Calculate autofocus position1000 derives the optimal focus setting by analyzing images of the samplewithin the cartridge, rather than by analyzing images of the cartridgeitself or images of other objects added to the sample. Specifically,calculate autofocus position 1000 analyzes images of a particle set thatincludes the particles to be counted, and other particles having thesame focusing properties, using an analysis algorithm similar to thatused to identify particles for counting. This ensures that the focusposition found by calculate autofocus position 1000 is close to thatoptimal for counting particles of interest.

Calculate autofocus position 1000 requires no data input but begins whena measurement field of a cartridge is in position for imaging within areader. Step 1010 of calculate autofocus position 1000 moves a focusadjustment to a first end of a range of focus adjustments. An example ofstep 1010 is controller 450 controlling focus mechanism 147 to moveimaging optics 140 of systems 100, 100′ to one end of its focus range.Step 1020 enables an illumination module to illuminate the measurementfield. Step 1030 records an image S of the measurement field. Examplesof steps 1020 and 1030 are controller 450 turning on illumination module200 or 300 and recording an image S generated by sensor 160, FIG. 2.Step 1040 runs a calculate autofocus metric routine on image S (e.g.,calculate autofocus metric 1100, FIG. 16). It is understood that step1040 may be executed in the sequence shown, or may be postponed untilsteps 1050, 1060 and 1070 are performed. That is, calculate autofocusmetric 1100 is a data analysis routine that can be performed either inreal time with acquisition of images S, or later after the imageacquisitions are complete. Step 1050 is a decision; if a second end ofthe range of focus adjustments has been reached, calculate autofocusposition 1000 advances to step 1070. If the second end has not beenreached, calculate autofocus position 1000 proceeds to step 1060, whichsteps focus adjustment to a position that is incrementally differentfrom the previous focus adjustment, then returns to step 1030 to recordanother image S. An example of step 1060 is controller 450 controllingfocus mechanism 147 to move imaging optics 140 of systems 100, 100′ toan incrementally different position in its focus range.

Step 1070 of calculate autofocus position 1000 disables the illuminationmodule that was enabled in step 1020. At this point, calculate autofocusposition 1000 has at least gathered an image S at a plurality of focuspositions; if steps 1040 corresponding to each image S have not beenperformed, they are now performed before proceeding to step 1080. Step1080 fits a Gaussian distribution to the autofocus metrics returned fromeach instance of step 1040, with respect to the focus adjustment valueassociated with each such instance. Step 1085 calculates the optimalfocus setting (for the illumination module enabled in step 1020) as thecenter focus setting with respect to the Gaussian distribution. In analternative embodiment, steps 1080 and 1085 are replaced by a step inwhich the optimal focus position is set to be the recorded position withthe optimal calculated autofocus metric.

An optional step 1090 of calculate autofocus position 1000 calculatesoptimal focus for an alternate illumination module, or for particlecounting, by adding an offset to the optimal focus setting calculated instep 1085. The offset added in step 1090 may for example correct forchromatic aberration expected in optics (e.g., imaging optics 140) dueto a wavelength change between two illumination modules. Also, as apractical matter, the offset added in step 1090 may correct for othereffects. Such effects may include, for example, mechanical hysteresis orbacklash in a focusing mechanism depending on the direction of movementof such mechanism. The offset may also be empirically derived betweenthe optimal focus setting calculated in step 1085, and a focus settingthat works ideally for particle counting purposes. For example, data maybe obtained during calibration of systems 100, 100′ that can be utilizedto empirically derive such an offset. Step 1095 of calculate autofocusposition 1000 thus returns at least the optimal focus setting calculatedin step 1085, and may also return other optimal focus settings ascalculated in step 1090.

FIG. 16 is a flowchart of a method called calculate autofocus metric1100 that provides a focus metric M for use by calculate autofocusposition 1000 to optimize focus position, as discussed above. Calculateautofocus metric 1100 may, for example, be executed by processor 460 ofsystems 100, 100′ to provide focus metric M for optics 140 relative toone measurement field of a sample within a cartridge (e.g., cartridges130, 130′, FIGS. 1-5, or cartridges 2600, 2600′, 2600″, FIGS. 45-50).Calculate autofocus metric 1100 derives focus metric M by analyzingimages of the sample within the cartridge, rather than by analyzingimages of the cartridge itself. However, it should be emphasized thatcalculate autofocus metric 1100 is but one way to derive a focus metric,and other ways of deriving a focus metric may be used in place ofcalculate autofocus metric 1100 in the context of step 1040 of calculateautofocus position 1000 described above.

Step 1105 of calculate autofocus metric 1100 receives an image S. Pixelsof image S have values according to the light intensity received by asensor at the corresponding location within the image. An example ofstep 1105 is receiving image S from calculate autofocus position 1000(e.g., when step 1040 of calculate autofocus position 1000 initiatescalculate autofocus metric 1100, as discussed above, it passes image Sto calculate autofocus metric 1100). Step 1110 creates a processedpseudo image F from S by utilizing S as input for a flat field masksubroutine. An example of step 1110 is creating pseudo image F from S byperforming flat field mask 1200, FIG. 17, discussed below. Flat fieldmask 1200 returns a binary image N wherein a pixel value of 1corresponds with likelihood of the corresponding pixel of S belonging toa particle, and a pixel value of 0 corresponds with likelihood of thecorresponding pixel of S not belonging to a particle.

At this point, it is noted that when this document discusses images andpixels thereof, the standard convention will be followed in which anupper case variable will be utilized for the image as a whole (e.g., S),and lower case variables will be utilized for pixels thereof (e.g., sx,yor s(x,y)). Also, certain techniques and parameters that are describedin terms of pixels herein are appreciated as sensitive to distance inobject space that a single pixel spans in image space. In this document,the term “image scale” is sometimes used as a reference to a distance inobject space that maps to the size of one pixel in a detected imagethereof. For example, if a system has an image scale of 2 μm/pixel, anobject with a physical length of 10 μm will span 5 pixels in an imagethereof.

Step 1120 calculates metric M by summing the square of each pixel of Sthat is associated with a pixel of F whose value is 1. That is, whenf(x,y)=1, the corresponding s(x,y) is squared and added to thesummation. This has the effect of increasing M when more pixels of S areidentified as belonging to particles to be counted (as determined byflat field mask 1200, as discussed below). It also increases M when thepixels that are counted are bright (the corresponding values of s(x,y)are large) thereby favoring particles in focus. Step 1125 returns M foruse by calculate autofocus position 1000.

FIG. 17 is a flowchart of a method called flat field mask 1200 thatgenerates binary mask N for use by calculate autofocus metric 1100 andby process source image 1300, as described further below. Flat fieldmask 1200 may, for example, be performed by processor 460 of systems100, 100′ to provide binary mask N, received by calculate autofocusmetric 1100 as F, for use therein. Flat field mask 1200 receives animage S as input and convolves the image with a filter kernel K to forma temporary pseudo image F that has zero or near-zero values for pixelsthat are likely associated with particles to be counted. Individualpixels are further enhanced in a temporary pseudo image N that divideseach pixel s by the corresponding f. Finally, pseudo image N isthresholded to provide a final binary image N wherein pixel values of 1correspond to image pixels that likely belong to a particle.

Step 1205 of flat field mask 1200 receives input image S and kernel K.Step 1210 creates a pseudo image F by convolving S with K. Filter kernelK is now discussed before completing the explanation of flat field mask1200.

FIG. 18A depicts an exemplary kernel K for use in step 1210 of flatfield mask 1200. It will be appreciated by one skilled in imageprocessing that a convolution of kernel K with an image S having blobsof relatively high intensity against a relatively dark background willgenerate a pseudo image having negative values associated with pixels ofthe blobs, but likely positive values elsewhere. It should be noted thatkernel K is set up in the expectation that the corresponding image hasan image scale of about 2 μm/pixel and that the particles of interestare about 10 microns across; therefore the region of K having negativecoefficients approximates a circle of diameter 5 pixels. Kernel K iscomposed of two contributions: a mean filter of radius 6 and anapproximately Gaussian kernel based on the size of the cells to bedetected. The mean filter component averages the image in an 11-pixelneighborhood and leads to a flat-field image centered around 1. The(negative) Gaussian component selectively identifies cell-sized featuresand results in large pixel values (the initial values of image N, asdescribed below in connection with step 1230) for a flat-field image inthe neighborhood of a cell. The relative strengths of the two componentsdetermines the amount of contrast that a cell must have relative to thebackground, to be detected. Both the absolute size of the kernel, (e.g.,the number of pixels in the kernel) as well as the size scale, inpixels, of the Gaussian contribution to the kernel scale with the sizeof the particle of interest in pixels. Hence, the absolute size of thekernel, in pixels, and the size scale, in pixels, of the Gaussiancontribution to the kernel scale with the physical size of the particleof interest and the image scale.

Exemplary kernel K shown in FIG. 18A is not normalized; the sum of theelements of K is equal to 25, such that convolution of an image with Kwill increase the net average values of pixels in the resulting image toincrease. A normalized kernel K′ shown in FIG. 18B could also beutilized; kernel K′ corresponds to K wherein each element is divided by25 such that the net average values of pixels in an image convolutedwith K′ remain the same (e.g., overall, the pixel values are multipliedby one).

Reverting to FIG. 17, after the convolution of S with K to form F, step1220 of flat field mask 1200 sets each pixel f to zero when f is lessthan zero. Thus, given the exemplary K and image conditions discussedabove, pixels likely associated with blobs will now have values of zeroor near zero while other pixels will have positive values. Step 1230generates a pseudo image N by dividing each pixel of S by thecorresponding pixel of F. Because the blob pixels have values of zero ornear zero, the corresponding pixels of N now have very large values, orvalues of infinity. Step 1240 modifies N by replacing any pixel n with avalue of 10 or greater with 1, and any pixel n with a value of less than10 with zero, to generate binary image N. It will be appreciated thatutilizing 10 as the cutoff value for binary image N is not the onlypossible choice; other suitable cutoff values may be determined byreviewing pseudo images N that are created in step 1230. However, giventhe field values of K shown in FIG. 18, the flat-field image will have avalue of approximately 1 in the absence of particles to be counted, so acutoff value at least greater than 1 would be required. Step 1245returns binary image N to the step that called flat field mask 1200.

FIG. 19 is a flowchart of an exemplary method called process sourceimage 1300 for processing a source image to provide a data structure ofparticles within the image. Process source image 1300 may be performed,for example, by processor 460 of systems 100, 100′, taking an image S asinput that is generated by sensor 160 of a measurement field of a samplein a cartridge 130 or 130′, and returning a data structure B of blobsidentified in S. It should be emphasized that the steps listed inprocess source image 1300 form an exemplary embodiment that shouldenable one skilled in the art to practice at least the method specified,however certain steps thereof are optional and need not always beexecuted in the manner or order described. In particular, it should beevident that certain subroutines, individual steps and groups of stepsmay serve to increase accuracy of particle counting methods, but couldbe modified or omitted to simplify processing or reduce cost.

Step 1305 of process source image 1300 receives image S as input. Step1310 calls a subroutine prefilter(S) that removes line noise, largescale features and electronic noise offsets in image S. An example ofstep 1310 is calling subroutine prefilter 1400, described below. Step1320 calls a subroutine remove background(S) that generates a pseudoimage SBG that subtracts local background both from background regionsand neighboring regions. An example of step 1320 is calling subroutineremove background 1500, described below. Step 1330 calls a subroutinedeclump (S, SBG) that identifies connected regions within pseudo imageSBG that may contain multiple particles to be counted, and splits theconnected regions for further processing. An example of step 1330 iscalling subroutine declump 1700, described below. Step 1340 generates abinary mask M by calling the flat field mask(S) subroutine describedpreviously. An example of step 1340 is calling flat field mask 1200.Step 1350 calls a subroutine morphological intersection (SBG,M) thatmodifies pseudo image SBG by filtering connected regions of SBG wherebinary mask M is 0 within an entire region. An example of step 1350 iscalling subroutine morphological intersection 1800, described below.Step 1360 generates a blob list B by using a subroutine find blobs (SBG)that identifies connected regions of bright pixels within pseudo imageSBG. Blob list B labels the pixels to identify which regions they belongto. An example of step 1360 is calling subroutine find blobs 1900,described below. Step 1370 calls a subroutine filter blobs (B) thatmodifies blob list B by calculating various statistical moments on theblobs therein, and removing blobs that do not fit criteria for a desiredparticle count. An example of step 1370 is calling subroutine filterblobs 2000, described below. Step 1375 returns blob list B for furtherprocessing.

FIG. 20 is a flowchart of an exemplary subroutine prefilter(S) 1400 thatremoves line noise, large scale features and electronic noise offsets inimage S. Prefilter 1400 may be performed, for example, by processor 460of systems 100, 100′, taking an image S as input that is generated bysensor 160 of a measurement field of a sample in a cartridge 130 or130′, and returning a modified image S. It is appreciated that the stepslisted for prefilter 1400 are exemplary only, and that certain of thesesteps may be omitted for cost savings or to reduce processingcomplexity, with possible impact on particle count accuracy.

Step 1405 of prefilter 1400 receives image S as input. Step 1410generates a temporary pseudo image F as a fast Fourier transform ofimage S, the fast Fourier transform (and its inverse) being known in theart. Step 1420 performs a high pass filtering operation on pseudo imageF by removing, in the frequency domain, low frequency content thatcorresponds to features larger than 100 pixels in the spatial domain.This removes image content that is too large to be considered as aparticle for counting; it is expedient to do this operation in thefrequency domain because of the difficulty in assessing large objects inthe spatial domain against a background of small objects. Also, it isunderstood that the present method desires to count particles on theorder of 10 μm in size with an image scale of 2 μm/pixel; the lowfrequency content removed in step 1420 would be adjusted accordingly toscreen out unreasonably large image content if smaller or largerparticles were to be counted. Step 1430 sets frequency components alongky=0 to zero except at k×=0; that is, any DC component that exists atky=k×=0 is maintained. Step 1430 therefore advantageously suppressesline-patterned dark noise that is often introduced by CMOS imagesensors. Step 1440 creates a new version of image S by performing aninverse fast Fourier transform on F as modified by steps 1420 and 1430.Step 1450 determines the minimum value within S and subtracts this valuefrom each pixel in S. Step 1455 of prefilter 1400 returns the modifiedimage S.

FIG. 21 is a flowchart of an exemplary subroutine remove background(S)1500 that generates a pseudo image SBG by subtracting local backgroundboth from background regions and neighboring regions. Remove background1500 may be performed, for example, by processor 460 of systems 100,100′, taking an image S as input that is generated by sensor 160 of ameasurement field of a sample in a cartridge 130 or 130′, or asprefiltered by prefilter 1400, and returning a pseudo image SBG.

The purpose of remove background 1500 is to calculate the localbackground in the area of each particle to be counted. The principle ofthe routine is to determine the dimmest separation lines between areasof local brightness, and then define the local background for each areaof local brightness as the maximum pixel value on the separation linesforming a perimeter therearound. The dimmest separation lines betweenareas of local brightness are equivalent to inverted watershed lines. Aglobal image processing method is utilized to determine a maximum valueof the separation line's perimeter around each area of local brightness.This method flood fills a pseudo image of the areas of local brightnessup to the maximum value for the separation line perimeter around areasof local brightness. Alternatively, each perimeter contour may be tracedout individually. Because local maxima may be introduced by noise,remove background 1500 blurs a temporary copy of the image to suppresssuch maxima for watershed line identification purposes. Also, indetermining the local background, it is desirable to treat clumped cellsas a single object (thus remove background 1500 is performed beforedeclump 1700, described below).

Step 1505 of remove background 1500 receives image S as input. Step 1510creates a pseudo image BL by applying a six-pixel radius Gaussian blurto image S. The radius of the Gaussian blur is chosen as 6 pixelsbecause the present method desires to count particles on the order of 10μm in size in images with a scale of 2 microns per pixel; it isunderstood that the Gaussian blur radius should be modified whenparticles that are significantly smaller or larger are to be counted orif a different image scale applies.

Also, it should be understood that in this case and in other casesherein, a Gaussian blur of radius r pixels is applied by convolving animage with a filter kernel containing values representative of a2-dimensional Gaussian distribution of radius r pixels. Forcomputational ease, the kernel may be truncated to consist only of thepixels that have significant values, e.g., a kernel of [r pixels]×[rpixels] may be used in the case of a Gaussian width of r pixels.Furthermore, the kernel may be normalized such that it integrates to 1,such that the effect of applying the blur is only to smooth the image,rather than scale it by increasing or decreasing the net intensity ofits pixels.

In step 1510, the purpose of the Gaussian blur is to avoid erroneouswatershed lines through the interior of a particle of interest due toshort-scale pixel intensity variation within the perimeter of theparticle. Such intensity variation can arise from, e.g., camera noise,light scattering artifacts, biological properties of the particle ofinterest, and the presence of other sample components within the sameregion of the image. The radius of the Gaussian blur is set toapproximately match the size of the particle of interest, and will thuschange with the physical size of the particle of interest, and withimage scale. This covers characteristic scales for short-scale interiorintensity variation. If only certain known characteristic scales arepresent, the radius of the blur applied in step 1510 can be adjustedaccordingly to be a closer match to the greater of the scales present.In cases where interior intensity variation of particles of interest isalready smooth, step 1510 can be eliminated altogether. Empiricaloptimization may be utilized to set the radius of the blur applied instep 1510.

Step 1520 creates a binary image W by calling a subroutine calculatewatershed lines (BL), described below as calculate watershed lines 1600.Step 1530 creates a still further pseudo image M that depends on thevalues of S and the value of binary image W for a corresponding pixeltherein. In step 1530, for each pixel coordinate (x, y), mx,y is set tothe corresponding value sx,y when wx,y=1, otherwise mx,y is set to 0(that is, mx,y=0 when wx,y=0).

Step 1540 of remove background 1500 creates a background image BG bycalling a subroutine morphological reconstruction (S,M), described belowas morphological reconstruction 1650. Step 1550 creates an output imageSBG by taking the maximum value of (S-BG) and 0 for each pixel locationin S and BG, that is, all negative values are converted to 0. Step 1555returns SBG.

FIG. 22 is a flowchart of an exemplary subroutine calculate watershedlines (BL) 1600 that generates a binary “watershed” image. Calculatewatershed lines (BL) 1600 may be performed, for example, by processor460 of systems 100, 100′, taking an image BL as input that is processedfrom an image S generated by sensor 160 of a measurement field of asample in a cartridge 130 or 130′, or as prefiltered by prefilter 1400and possibly smoothed by step 1510 in remove background 1500.

Step 1605 of calculate watershed lines 1600 receives an image BL asinput (e.g., image BL as generated at step 1510 of remove background1500, described above, or image BL as generated at step 1710 of declump1700, described further below). Step 1610 generates a pseudo image Wfrom image BL by calculating a watershed as described in “Watersheds inDigital Spaces An Efficient Algorithm Based on Immersion Simulations”[Vincent (1991)]. In Vincent, an input image is segmented intowatersheds, or catchment basins, surrounding local maxima and labeled bythe catchment basin that each pixel belongs to. A “watershed” label issometimes inserted into the image to separate the catchment basins (bythe description above, it may be seen that the separation lines are moreanalogous to separations between watersheds, than watershedsthemselves). Step 1610 always separates adjacent basins by applying thewatershed label (that is, Vincent's routine is modified to always applythe label, rather than applying it only sometimes). Step 1620 modifiespseudo image W to create a binary image by converting all of thewatershed labels to pixel values of 1, and all other pixels to 0. Step1625 returns binary image W.

FIG. 23 is a flowchart of an exemplary subroutine morphologicalreconstruction (S,M) 1650 that calculates a grayscale morphologicalreconstruction of an input image. Morphological reconstruction 1650 maybe performed, for example, by processor 460 of systems 100, 100′, takingan image S and a marker image M as input (image S may be generated bysensor 160 of a measurement field of a sample in a cartridge 130 or130′, or as prefiltered by prefilter 1400, while marker image M is aprocessed pseudo image generated, for instance, by step 1340 in processsource image 1300).

Step 1655 of morphological reconstruction 1650 receives images S and Mas input (e.g., image S as generated by sensor 160 or as prefiltered byprefilter 1400, and M as generated at step 1530 of remove background1500, described above). Step 1660 returns a grayscale morphologicalreconstruction as described in “Morphological Grayscale Reconstructionin Image Analysis: Applications and Efficient Algorithms” [Vincent(1993)]. Step 1665 returns modified image S.

FIG. 24 is a flowchart of an exemplary subroutine declump (S,SBG) 1700that separates multiple particles to be counted into separate imageregions. Declump 1700 may be performed, for example, by processor 460 ofsystems 100, 100′, taking images S, SBG as input (image S may begenerated by sensor 160 of a measurement field of a sample in acartridge 130 or 130′, or as prefiltered by prefilter 1400, while imageSBG is a processed pseudo image with local background subtracted out,e.g., as generated by remove background 1500).

Step 1705 of declump 1700 receives images S and SBG as input (e.g.,image S as generated by sensor 160 or as prefiltered by prefilter 1400,and SBG as generated at step 1320 of process source image 1300,described above). Step 1710 generates a pseudo image BL by applying aGaussian blur to image S. The radius of the blur applied in step 1710 istypically two to three pixels, and is set to suppress noise within asingle particle in order to avoid splitting the particle into multipleparticles, and without introducing any possibility of blurring out awatershed line between two particles. That is, this blurring stepsuppresses short-scale intensity variation within the perimeter of aparticle of interest. Causes for such intensity variation have beendiscussed above in connection with FIG. 21, step 1510. In the case ofstep 1710, the blurring further serves to avoid watershed lines inbetween two or more particles that are sufficiently close to each otherthat the pixel intensities do not reach the true background level inregions located between the particles. The value for the radius of theGaussian scales with the image scale. Since this routine also serves tokeep together very close-lying particles, the radius value also dependson the apparent sharpness of the particles in the image. The sharpnessis affected by, e.g., optical aberrations, pixel resolution, sensorelectronics performance, light transmission properties of the sample andcartridge materials through which imaging is performed, and inherentlight emission profile of the particle.

Step 1720 generates a binary image W by passing BL to subroutinecalculate watershed lines 1600, discussed above. Step 1730 modifies SBGby leaving each pixel sBGx,y undisturbed except for pixels where Windicates a watershed line, in which case the corresponding pixel sBGx,yis set to 0. Step 1755 returns modified image SBG.

FIG. 25 is a flowchart of an exemplary subroutine morphologicalintersection (SBG,M) 1800 that filters connected regions of input pseudoimage SBG where input marker file M is zero within an entire region.Morphological intersection 1800 may be performed, for example, byprocessor 460 of systems 100, 100′, taking images S and M as input(image S may be, for example, a processed pseudo image while marker fileM may be a file coded to separate regions of interest from regions notof interest).

Step 1805 of morphological reconstruction 1650 receives images S and Mas input (e.g., image SBG as generated at steps 1320 and 1330 of processsource image 1300, and marker file M as generated at step 1340 ofprocess source image 1300, as described above). Step 1810 creates atemporary binary image SM wherein for each pixel coordinate x,y, sM x,yis set to a value of 1 where s x, y has a value of at least 1, otherwisesM x, y is set to a value of 0. Step 1820 calls morphologicalreconstruction 1650 to calculate a grayscale morphologicalreconstruction of image SM utilizing marker file M. Step 1830 modifiesinput file S by setting each pixel s x,y to 0 where sM x, y already hasa value of zero. Step 1835 returns modified image S.

FIG. 26 is a flowchart of an exemplary subroutine find blobs(S) 1900that creates a blob list B of data structures that include a label foreach connected region in S and the coordinates of nonzero pixels in Sthat belong to each of the connected regions. Find blobs 1900 may beperformed, for example, by processor 460 of systems 100, 100′, taking animage S as input (image S may be, for example, a processed pseudoimage).

Step 1905 of find blobs 1900 receives image S as input (e.g., image SBGas generated at step 1350 of process source image 1300, as describedabove). Step 1910 generates blob list B utilizing a blob extraction suchas is known in the art and is generally called connected-componentlabeling.

Connected-component labeling consists of identifying connected regionsof foreground pixels. In the present embodiment, a foreground pixel inimage SBG is a pixel of value 0 while pixels of value 1, i.e. watershedlines, are background pixels. The connected-component labeling methodserves to assign a unique label to each region of connected foregroundpixels, i.e., blobs. In the present embodiment, connected-componentlabeling has been implemented as follows. A label counter and an emptyqueue are initialized, and a row-major scan is performed on image SBG.If an unlabeled foreground pixel is encountered, the label value isincremented and the pixel is added to the queue. This operationinitiates a subroutine that serves to identify all pixels belonging to aconnected region. In the subroutine, the first pixel in the queue isassigned the current label value. This pixel is then removed from thequeue, and all its unlabeled foreground neighbor pixels are added to thequeue (“neighbor pixels” herein are the 8 pixels closest to the pixel ofinterest, known from graph theory as 8-connectivity). This repeats untilthe queue is empty, at which point the current label value has beenassigned to all pixels belonging to this connected region, and theprocess exits the subroutine. The scan continues to search for the nextunlabeled foreground pixel, which will lead to the identification ofanother connected region. The scan ends when all pixels in SBG have beenscanned.

Connected-component labeling can thus be described by the followingpseudocode in Fortran-style:

FOR each foreground pixel in image SBG (following row-major scan)  IFpixel is unlabeled   Increment label counter   Add foreground pixel toqueue   WHILE queue not empty    Assign label to first pixel in queue   FOR all foreground neighbor pixels     IF pixel unlabeled     Addpixel to queue     ENDIF    ENDFOR    Remove pixel from queue   ENDWHILE ENDIF ENDFOR

After step 1910 is complete, step 1915 returns blob list B.

FIGS. 27A through 27C are flowcharts of an exemplary subroutine filterblobs (B) 2000 that filters blob list B of data structures that includea label for each connected region in S and the coordinates of nonzeropixels in S that belong to each of the connected regions. Filter blobs2000 may be performed, for example, by processor 460 of systems 100,100′, taking blob list B as input.

Generally speaking, filter blobs 2000 applies moment-of-inertia typestatistical measures to filter out blobs that do not behave as theparticles intended to be counted. A number of the specific values usedas screens may be set by considering the size of particles intended tobe counted, and by analyzing images of samples and adjusting the valuesto include particles and exclude artifacts appropriately. The specificembodiment shown in FIGS. 27A through 27C applies to images with a scaleof 2 microns per pixel and particles of interest with a diameter ofabout 10 microns. Meaningfulness of many of these tests is also enhancedby subtracting the background (e.g., as done in remove background 1500,described above) or by utilizing threshold-subtracted integrateddensity, or TSID, on a blob by blob basis, as discussed below. Oneskilled in the art will see that the successive screens of filter blobs2000 are applied to the input blob list to remove blobs that areconsidered inappropriate as candidates for particle counting; howeverthese screens are exemplary only and presented in an exemplary order.Therefore, these screens may be rearranged in order, or even deleted forcost savings or to reduce processing complexity. None of the particularscreens described is considered essential.

Step 2005 of filter blobs 2000 receives blob list B as input (e.g., bloblist B as generated at step 1910 of find blobs 1900, as describedabove). Step 2007 initiates a loop that increments through each blob inblob list B. Step 2010 counts the pixels in the blob. This filteringstep is to remove artifact blobs that arise from “hot” (undulysensitive) sensor pixels. Step 2012 determines whether the number ofpixels is equal to 1 (and because of the rationale underlying steps 2012and 2014, the value of 1 is appropriate for any pixelated system andwill not scale with image scale or particle size). If so, step 2014removes the blob from B and filter blobs 2000 advances to step 2050. Ifnot, filter blobs 2000 advances to step 2020.

Step 2020 calculates a best fit ellipse of pixel values in the blob;that is, step 2020 calculates major and mirror axes a and b of the bestfit ellipse. It should be noted that a and b are not limited to integervalues, as the blob may be small and/or oriented at an angle withrespect to horizontal and vertical axes of the imager. The intent ofthis screen is to remove blobs caused by residual hot pixels (e.g., hotpixels combined with other background effects), clumped hot pixels, andvery small events caused by background effects.

Step 2022 determines whether the area defined by 4 ab is less than aminimum area. For a system with image scale of 2 μm/pixel, the minimumarea may be about 2 pixels. Unless clumping of hot pixels is the onlysource of small, false events, the minimum area scales with the imagescale. This value depends on the density of hot pixels, as a highdensity of hot pixels would increase the probability of clumping ofmultiple hot pixels, in which case the cut would likely have to beincreased beyond 2 pixels. The minimum area also depends on the size ofthe particles of interest as well as the size and relative frequency ofsmaller, false events. The size histograms for particles of interest andsmall, false events may or may not overlap. In either case, the cutshould be placed to average a net zero error in the count of particlesof interest. If the particles of interest are significantly larger thanabout 5 pixels, the minimum area can be increased to improve therejection of background artifacts, including smaller particles not ofinterest. If any short-scale background features are present in additionto hot pixels, the performance will likely be degraded if the cut shouldbe reduced, in which case step 2022 could be removed. The minimum areaalso depends on the typical size scale of background features. If thetypical size scale of background features is closer to the size of theparticles of interest and the relative frequency of such backgroundfeatures is significant, it may be difficult to achieve satisfactoryperformance. In that case, it may be advantageous or necessary toimprove the image resolution by, for instance, decreasing the imagescale or utilizing a higher-performance imaging system.

If the ellipse area 4 ab is less than the minimum area, step 2024removes the blob from B and filter blobs 2000 advances to step 2050. Ifnot, filter blobs 2000 advances to step 2026.

Step 2026 determines whether the area defined by 4 ab from step 2020 isgreater than a maximum area that may be, for example, 100 pixels. Thisscreen is set up to conservatively remove events that are much largerthan particles of interest, and may be increased to about 100 sinceother area filters applied in steps 2036 and steps 2044, discussedbelow, also serve to remove events larger than the particles ofinterest. The purpose here is to make the best cut in a histogram wherea true population and a false population may exist. In the present case,the false events are larger than the true events. The maximum area maytherefore scale with the image scale and the size of the particles ofinterest. In systems where the occurrence of large, false events isrelatively rare, no significant performance changes may be expected byvarying the maximum area over a wide range.

If the ellipse area 4 ab is greater than the maximum area, step 2024removes the blob from B and filter blobs 2000 advances to step 2050. Ifnot, filter blobs 2000 advances to step 2030.

The ellipse fit performed in step 2020 can be biased by long “tails”associated with certain blobs. The area limits in decision steps 2022and 2026 above are accordingly loose so that valid particles are notfiltered out. A further filtering step compensates for this by utilizinga similar technique based on the 4th power of pixel intensities. Step2030 calculates a best fit ellipse of the 4th power of pixel values inthe blob; that is, step 2020 calculates major and mirror axes a and b ofthe best fit ellipse formed by the 4th power of the pixel values. Theeccentricity of this ellipse is defined as sqrt(1−(b/a)2). Blobs inimages may have outlying regions of lower intensity caused by image orimaging artifacts. For instance, local background variation at or veryclose to a particle may not be distinguished from the actual particle.Hence, a blob may include an intensity contribution from localbackground in addition to the intensity contribution from the particle.Particle movement during at least a portion of the image exposure,caused for instance by general sample motion, may produce an additionallower intensity contribution to the blob. Such an effect may also becaused by mechanical motion of the cartridge or of one or more imagingsystem components Likewise, aberrations in the imaging system canproduce, e.g., uniform blur, directional tails of lower intensity, andhalos, all of which may be included in a blob. When determining theshape and size of a particle, it is advantageous to reduce or eliminatethe contribution from artificial outlying regions of lower intensity.This can be achieved, for instance, by raising the pixel intensities toa greater power, which reduces the weight of lower intensity pixels. Inan embodiment, the pixels values are raised to the 4th power. For othersystems with different image or imaging properties, a different powermay be optimal. If the images are free of artificial, outlying regionsof lower intensity, raw pixel values may be used. When CD4+T-helpercells are the particles of interest, the eccentricity based screenremoves events that are clearly too eccentric to originate from anapproximately circular particle (e.g., a CD4+T-helper cell).

Step 2032 removes events that are clearly too eccentric to originatefrom an approximately circular particle. The applicability of thecalculated eccentricity is highly dependent on resolution of the imagingsystem. In an embodiment where a particle of interest has a diameter ofonly about 5 pixels, the eccentricity limit has to be relatively loose,such as 0.8. In a system with improved resolution relative to theparticle size, the eccentricity limit can be made tighter (lower). Theeccentricity limit depends on the types of artifacts present in theimage. The optimal eccentricity limit is the value that, on average,leads to a net zero error in particle count. In an embodiment, a cutvalue in the range 0.75-0.85 has been found to be optimal.

Therefore, in an embodiment, step 2032 determines whether theeccentricity of the ellipse exceeds 0.8. If so, step 2034 removes theblob from B and filter blobs 2000 advances to step 2050. If not, filterblobs 2000 advances to step 2036. The eccentricity based screen isdependent on resolution of the imaging system utilized (e.g., sensor160's rendition of an image that is magnified by optics 140). In anembodiment wherein particles to be counted have a diameter of only about5 pixels, a cutoff value used for an eccentricity screen must be loose(e.g., a range of 0.75 to 0.9) wherein if resolution of an imagingsystem was such that a typical particle to be counted had a largerdiameter, a tighter (lower) limit could be utilized.

Step 2036 determines whether the area defined by 16 ab is greater than asize limit. Step 2036 removes events that are clearly too large to be aparticle of interest, but because more refined screen of particle sizeis performed following this step (steps 2040 to 2046, discussed below)the size limit is set conservatively loose. The screen implemented instep 2036 does, however, improve the quality of the input data to, andtherefore the performance of, the procedure that follows in steps 2040to 2046. In an embodiment, a size limit of approximately 50 has beenfound to work well. Due to the presence of a more refined size selectionprocedure following this step, the size limit value is not critical. Thevalue of the size limit scales with the image scale and the size of theparticles of interest.

Therefore, in an embodiment, step 2036 determines whether the areadefined by 16 ab is greater than 50 pixels. If so, step 2034 removes theblob from B and filter blobs 2000 advances to step 2050. If not, filterblobs 2000 advances to step 2040.

An entropy based threshold can be utilized to remove residual backgroundassociated with each blob such that legitimate particles will still becounted but artifacts can be screened out. The intent of the followingsteps is to create the best estimate of particle size and to craftlimits around the size to account for natural variation of theparticles, noise, resolution effects, and optical blurring.

Step 2040 first calculates an entropy based threshold utilizing the“Kapur, Sahoo, and Wong Method” described in the paper, “A Survey ofThresholding Techniques” by P. K. Sahoo, S. Soltani and A. K. C. Wong,published in Computer Vision, Graphics, and Image Processing 41, at page237. However, instead of applying the entropy based threshold globallyas in this paper, the threshold is applied locally on an individual blobbasis. Generally speaking, this method defines the probabilities oforiginal gray level distributions as pi where i is a particulargrayscale value out of 1 possible levels in a grayscale range G (e.g.,an integer within the range of 0 to 1-1) and a variable Pt as

$P_{t} = {\sum\limits_{i = 0}^{t}\; p_{i}}$

for a given threshold candidate t. Further variables H_(b)(t) andH_(w)(t) are calculated as

${H_{b}(t)} = {- {\sum\limits_{i = 0}^{t}\; {\frac{p_{i}}{P_{t}}{\log_{e}( \frac{p_{i}}{P_{t}} )}}}}$and${H_{w}(t)} = {- {\sum\limits_{i = {t + 1}}^{l}\; {\frac{p_{i}}{1 - P_{t}}{{\log_{e}( \frac{p_{i}}{1 - P_{t}} )}.}}}}$

Finally, an optimal threshold t* is calculated as the gray level thatmaximizes H_(b)(t)+H_(w)(t), that is,

t*=ArgMax{H _(b)(t)+H _(w)(t)} for tεG.

Step 2040 also calculates a number called TSID as thethreshold-subtracted integrated density of all pixels in the blob beingprocessed, adds the TSID to the data structure of the corresponding blobin blob list B, and calculates the thresholded area of the blob.

Step 2042 determines whether the thresholded area is less than 11pixels. If so, step 2046 removes the blob from blob list B and filterblobs 2000 advances to step 2050. If not, filter blobs 2000 advances tostep 2044. Step 2044 determines whether the thresholded area is greaterthan 100 pixels. If so, step 2046 removes the blob from blob list B andfilter blobs 2000 advances to step 2050. If not, filter blobs 2000advances to step 2050 without removing the blob. Both the lower arealimit used in step 2042 and the upper limit used in step 2044 depend onparticle size, natural variation, noise, resolution effects, and opticalblurring. That is, the best estimate of the actual particle area isprovided by step 2040. The previous filtering on particle size hasimproved the data that is input to other steps in the process, or hasreduced processing time by removing events that clearly are notassociated with particles of interest. The lower area limit used in step2042 and the upper limit used in step 2044 represent the size range ofthe particles of interest with an additional tolerance to account forimperfections due to, e.g., noise, limited resolution, and blur. Thelower area limit used in step 2042 and the upper limit used in step 2044scale with the image scale and the size of the particles of interest.

Step 2050 determines whether all blobs have been processed through thefilters of filter blobs 2000 discussed above. If not, filter blobsreturns to step 2005 to process the next blob in B. If all blobs havebeen processed, filter blobs continues to step 2060.

Step 2060 sets a variable IQR to the inter-quartile range of all TSIDsof blobs in blob list B, and a variable Q3 to the third quartile valueof all TSIDs of blobs in blob list B. Step 2070 sets a scaling factorSCALE to 7, and a bright object threshold TB to Q3+IQR*SCALE. SCALE isan empirically determined parameter that may lie within the range ofabout 3 to 8. TB is approximately where the top value of the TSIDdistribution would have been, based on the bulk of the blob population,except for abnormally bright objects such as inclusions skewing the topend of the distribution. Thus, the loop defined by steps 2080 through2094 filters a histogram of blob brightness. Images may contain multipledifferent classes of particles, each characterized by a typical blobbrightness range. If the ranges are distinct or only partially overlap,it may be possible to separate the individual populations by makingsimple cuts in the histogram. In cases of overlap, TB may be set tominimize the number of blobs assigned to the wrong population. Forexample, in an embodiment the histogram contains the primary population,containing the particles of interest, and a class of extremely brightoutliers. The overlap is statistically insignificant and TB can beplaced using a simple inter-quartile approach. In this embodiment, thevalue of TB can be in the range from 4 to 8 and especially 7. In otherembodiments with statistically significant overlap between populations,a narrower range may be required. Also, in some cases, more refinedmethods such as peak fitting may be applied to correctly assign blobs toindividual populations.

Step 2080 initiates a loop that covers each blob in B. The next blob inB is considered in step 2090. A decision step 2092 determines whetherTSID of the current blob exceeds TB. If so, step 2094 removes the blobfrom B. If not, and/or after step 2094, a decision step 2096 determineswhether further blobs remain in B to be processed. If so, filter blobs2000 returns to step 2090 for the next blob. If not, step 2098 returnsthe modified blob list B.

FIGS. 28A and 28B are flowcharts of an exemplary subroutine correlatesource images (BA, BB) 2100 that takes blob lists BA, BB as input andgenerates a blob list BC of data structures that include only blobs thatare spatially correlated to one another. Correlate source images 2100may be performed, for example, by processor 460 of systems 100, 100′,taking blob lists BA, BB that were generated from a given measurementfield, utilizing two different illumination modules, as input. Generallyspeaking, correlate source images 2100 identifies objects that arewithin a fixed distance from each other, and identifies the “best” matchof such objects if multiple possibilities exist. One skilled in the artwill see that correlate source images 2100 applies a series of tests topotential combinations of blobs in the input blob lists, to match blobsthat are considered optimum matches for each other as candidates forparticle counting. However, these tests are exemplary only and presentedin an exemplary order; these tests may be rearranged in order or evendeleted for cost savings or to reduce processing complexity, and none ofthe particular tests described is considered essential.

Step 2105 of correlate source images 2100 receives blob lists BA, BB asinput. For example, each of blob lists BA, BB may be blob lists asgenerated from a measurement field within a cartridge 130 or 130′ imagedto sensor 160 and processed using the process source image 1300 method,as described above, with BA and BB being blob lists from the samemeasurement field utilizing different illumination modules 200, 300.

Step 2110 initializes a loop spanning each blob in BA; the remainingsteps of correlate source images 2100 determine whether there is a matchin BB for each such blob, and if a match is found, whether it is thebest available match. Step 2120 determines a position of the next blobba(1) to be considered in BA, and defines a blob bb(1) as the first blobin BB. Step 2130 initializes a loop spanning each blob bb in BB. Step2140 determines a position of the next blob bb to be considered in BB,calculates a variable DISTANCE1 between the position of blobs ba(1) andbb, and calculates a variable DISTANCE2 between the position of blobsba(1) and bb(1). Step 2142 is a decision step that determines whetherDISTANCE2 is greater than DISTANCE1. If so, step 2144 sets blob bb(1) asthe current blob bb. If not, or after step 2144, step 2146 determineswhether all blobs in BB have been processed, and returns to step 2140until all blobs BB have been processed. In this manner, steps 2130through 2146 find at least the best spatially matched blob bb(1) for thecurrent blob ba being processed, and identifies the distance DISTANCE2between bb(1) and ba.

Step 2152 is a decision step that determines whether DISTANCE2 isgreater than 16 μm. The choice of 16 μm as the maximum for DISTANCE2reflects an expected maximum spatial registration tolerance betweenimages from which blob lists BA, BB were generated and may vary inembodiments within a range of 12 to 20 microns. This allows forregistration shifts between the location of a particle as imaged underdifferent illumination sources. Such shifts can be caused by, e.g.,chromatic aberration, mechanical shifts between or during exposures, orparticle movement within a cartridge. In an embodiment, the choice of 16μm as the maximum for DISTANCE2 limits such shifts to a magnitude whereit is possible to generate an initial set of correlated blobs imagedunder different illumination sources with satisfactory reliability usinga simple correlation distance. The value of DISTANCE2 should be setlarge enough to encompass the registration shifts characteristic of thesystem, which may be approximately twice the size the particle ofinterest. This allows for the inclusion of some false correlations wherethe blobs originate from different particles. The optimal value ofDISTANCE2 may also depend on parameters including particle size,magnitude of registration shifts, and particle density. A more refinedanalysis of the distance between the blobs in a correlated pair,discussed in connection with FIG. 29A, serves to remove blobs due tofalse correlations. In embodiments with greater shifts or higherparticle density, correct correlation may be achieved using moreadvanced image registration methods, for instance relying on fiducialmarkers on the cartridge or recognition of corresponding particlepatterns in the two images.

In an embodiment, If DISTANCE2 is greater than 16 μm, blobs ba(1) and bbare at least an initial match, and correlate source images 2100 advancesto step 2170 for further processing. If DISTANCE2 is less than 16 μm,blobs ba(1) and bb are not a match, and correlate source images 2100advances to step 2160. Step 2160 is a decision step that determineswhether all blobs in BA have been processed. If no, correlate sourceimages 2100 returns to step 2120 to try to find matches for another blobba(1). If yes, correlate source images 2100 terminates at step 2165,returning blob list BC (defined below) as a list of blobs that correlateacross blob lists BA and BB.

Step 2170 is reached only when there is a preliminary, acceptable matchbetween blobs ba(1) and bb. At this point, further processing is done todetermine whether the preliminary match is the best match, or whetherthere are better matches in BA for blob bb than blob ba(1). Step 2170identifies blob ba as blob ba(1). Step 2172 initializes a loop spanningall blobs in BA; in this loop the blob being processed is identified asba(2). Step 2175 determines a position of the next blob ba(2), andcalculates a variable DISTANCE3 between the position of blobs ba(2) andbb. Step 2180 is a decision step that determines whether DISTANCE3 isless than DISTANCE2 (which was established as the distance between blobsba(1) and bb in step 2140). If DISTANCE3 is not less than DISTANCE2,blob ba(2) is not a better match for blob bb than blob ba(1), socorrelate source images 2100 advances to step 2190. But if DISTANCE3 isless than DISTANCE2, blob ba(2) is a better match for blob bb than blobba(1). In this case, correlate source images 2100 advances to step 2185,wherein blob ba(2) is identified as the (current) “best match” of blobbb, by setting blob ba as blob ba(2) and setting DISTANCE2 as DISTANCE3.Thus, further blobs ba will not only have to be closer to blob bb thanthe first blob ba(1) to be considered the best match for bb, they willhave to be closer to bb than ba(2).

After step 2185, correlate source images 2100 advances to step 2190,another decision step that determines whether all blobs in BA have nowbeen processed against blob bb. If not, correlate source images 2100returns to step 2175 to try to find a better match for blob bb. If so,correlate source images 2100 advances to step 2192.

Step 2192 is a decision step that determines whether blob ba remains thesame blob ba(1) that was found to be an initial match for blob bb(1) insteps 2130 through 2152. If not, correlate source images 2100 reverts tostep 2160 without adding anything to the correlated blob list (becausethe better match ba will eventually be found as the outer loop beginningat step 2110 advances to the appropriate ba). If ba remains the sameblob ba(1), correlate source images 2100 advances to step 2194, whereblobs ba(1) and bb(1) are added to blob list BC with an indication thatthey are correlated blobs. From step 2194, correlate source images 2100advances to step 2160, discussed above, to finish looping throughcandidate blobs from BA and BB.

The correlation method discussed in relation to FIGS. 28A and 28B may beextended to correlation of more than two blob lists. This would berelevant for systems requiring correlation of events from more than twoimages. Exemplary embodiments include particle identification systemssuch as system 100, FIG. 2 (reference to system with separate beampaths) and system 100′, FIG. 8, with additional illumination sourcesand/or conditions. For instance, a three-color fluorescence system usedfor counting CD4 and CD8 cells could be based on separate detection ofCD3, CD4, and CD8 positive cells but would require correlating eventsfrom three images, e.g., correlating three blob lists.

Events from more than two blob lists may be correlated by applyingcorrelate source images 2100 as described above, to pairs of blob lists.In an embodiment, blob lists A, B, and C are correlated. First, bloblists A and B are correlated using correlate source images 2100. Thisgenerates a blob list AB(A,B) containing correlated events. In thisdiscussion, the notation I(I,J) means a blob list representing blobsfound only in blob list I based on correlation of input blobs lists Iand J, and IJ(I,J) means a blob list representing blobs correlatingbetween blob lists I and J based on correlation of input blob lists Iand J. Remaining uncorrelated events from lists A and B are placed inblob lists A(A,B) and B(A,B). Next, each event in correlated blob listAB(A,B) is assigned an image location, e.g., pixel coordinates, as theaverage image location of the two correlated blobs from lists A and Brespectively. Blob list C is now processed three times by correlatesource images 2100 to correlate it with blob lists A(A,B), B(A,B), andAB(A,B). Correlation of blob lists C and A(A,B) leads to the generationof blob lists AC(A,B,C), A(A,B,C), and C(A,C), where blob list AC(A,B,C)contains blobs that spatially correlate across A and C but not B, bloblist A(A,B,C) contains blobs from A that did not spatially correlatewith blobs from B or C, and blob list C(A,C) contains blobs from C thatdid not spatially correlate with blobs from A Likewise, correlation ofblob lists C and B(A,B) leads to the generation of blob lists BC(A,B,C),B(A,B,C), and C(B,C). Correlation of C with AB(A,B) leads to thegeneration of blob lists ABC(A,B,C), AB(A,B,C), and C(AB,C), where bloblist ABC(A,B,C) contains blobs that spatially correlated in A, B, and C,blob list AB(A,B,C) contains blobs that spatially correlated in A and Bbut not C, and blob list C(AB,C) contains blobs from C that did notspatially correlate with AB. Finally, the events from C(A,C), C(B,C),and C(AB,C) are combined into a single blob list C(A,B,C) containing allthe blobs in C that did not spatially correlate with blobs in A or B.The final output, representing all two-way and three-way correlationsand remaining uncorrelated blobs, consists of blob lists ABC(A,B,C),AB(A,B,C), AC(A,B,C), BC(A,B,C), A(A,B,C), B(A,B,C), and C(A,B,C).Throughout, image locations for correlated blobs are set to the averageof the individual image locations as defined in the original blob listsA, B, and C.

If a fourth blob, D, list is present, as would be generated in afour-color system, blob list D could then be correlated with blob listsABC(A,B,C), AB(A,B,C), AC(A,B,C), BC(A,B,C), A(A,B,C), B(A,B,C), andC(A,B,C) generated above, also using correlate source images 2100.

FIGS. 29A and 29B are flowcharts of an exemplary subroutine compensatechromatic aberration (B) 2200 that takes a correlated blob list B asinput and modifies it by removing blobs that are outliers in terms ofexpected spatial matching between two source images. Compensatechromatic aberration 2200 may be performed, for example, by processor460 of systems 100, 100′, taking blob list BC, discussed above inconnection with FIGS. 28A and 28B, as input. Generally speaking,compensate chromatic aberration 2200 tightens the matching criteria foracceptable correlation between blobs identified utilizing differentillumination modules, by eliminating blobs that are outliers of thecorrelated blob population in terms of spatial shifts. Thus, compensatechromatic aberration 2200 allows “typical” registration shifts, asdefined by the blob population itself, but screens out blobs withatypical registration shifts from one image to another. The “typical”registration shifts may occur due to, e.g., chromatic aberration effectsbetween the fluorescence wavelengths in which the images are produced,localized sample heating due to light power used to illuminate thesample, evaporation, and mechanical shifts.

Step 2205 of compensate chromatic aberration 2200 receives blob list Bas input. For example, blob list B may be a correlated blob listgenerated by the correlate source images 2100 method, as describedabove, wherein B contains information of matched blobs ba, bb. Step 2210sets up a loop spanning all correlated blobs (ba, bb) in B. Step 2220calculates registration shifts Δx, Δy for the next blob (ba, bb), addsΔx, Δy to a temporary set of Δx, Δy and adds Δx, Δy to the informationassociated with blob (ba, bb) in B. Step 2230 is a decision step thatdetermines whether all blobs (ba, bb) in B have been processed; if not,compensate chromatic aberration 2200 returns to step 2220 to processanother blob (ba, bb), and if so, compensate chromatic aberration 2200advances to step 2240. Step 2240 calculates a (Q3−Q1 Δx INTERVAL) and a(Q3−Q1 Δy INTERVAL) from the set of Δx, Δy established by step 2220, andsets up a temporary blob list B′ that is initially equal to blob list B.

Step 2250 sets up a loop that is performed for each blob in B′. Steps2252 and 2256 are decision steps that determine whether Δx or Δy for agiven blob exceeds 1.5 times the respective calculated (Q3−Q1 ΔxINTERVAL) and (Q3−Q1 Δy INTERVAL). These steps remove false correlationsand/or outliers from B′ such that the fit performed in step 2260 (FIG.29B, discussed below) is based on typical, true correlations only.Hence, the choice of the values retained for the correlation is biasedsuch that false correlations will definitely be discarded, and evenpotentially true correlations that are outliers may be discarded.Therefore a standard inter quartile-based outlier rejection method withrelatively tight criterion, such as a multiplication factor in the rangefrom 1 to 2, results in good performance. In an embodiment, amultiplication factor of 1.5 is applied.

If the answer to either of the decisions in steps 2252 and/or 2256 isyes, compensate chromatic aberration 2200 advances to step 2254, whichremoves blob (ba, bb) from B′. After step 2254, or if steps 2252 and2256 are answered no, compensate chromatic aberration 2200 advances tostep 2258, another decision step that determines whether all blobs (ba,bb) in B′ have been processed. If not, compensate chromatic aberration2200 returns to step 2250 to process another blob (ba, bb) in B′, and ifso, compensate chromatic aberration 2200 advances to step 2260.

Step 2260 generates linear fit functions FX(x) and FY(y) from theinformation associated with each blob (ba, bb) in B′ by correlating Δxto x position and Δy to y position, respectively. This enables screeningof blobs (ba, bb) on the basis of a fit the normal shift of a blob inboth dimensions based on its position; this is useful because spatialshift effects may depend on initial position of a blob within ameasurement field.

Having set up linear functions FX(x) and FY(y) based on the blobs withthe most typical registration shifts as discussed in connection withsteps 2240 through 2260, compensate chromatic aberration 2200 discardstemporary blob list B′ and utilizes FX(x) and FY(y) for furtherscreening of blob list B. Step 2270 sets up a loop that spans allcorrelated blobs (ba, bb) in B. For a given blob (ba, bb), step 2280calculates FX(x) and FY(y) from the x, y position of each blob, anassociated dx=Δx−FX(x) and dy=Δy−FY(y), and a two-dimensional residualdisplacement 2D_RESIDUAL_DISPL=sqrt (dx2+dy2). Step 2282 is a decisionstep that determines whether 2D_RESIDUAL_DISPL is greater than 8 μm. Asin similar screening values discussed above, the value of 8 μm used instep 2282 depends on the size of the particles being counted, thepossibility of random movement and on the expected maximum spatialregistration tolerance between images from which blob lists BA, BB weregenerated. The screening value of 8 μm could vary in other embodimentswithin a range of about 6 μm to 10 μm. If step 2282 determines that2D_RESIDUAL_DISPL is greater than 8 μm, blob (ba, bb) is removed from Bin step 2284. This is another screen based on shifts between thelocation of blobs imaged with different illuminators associated with thesame particle. 2D_RESIDUAL_DISPL is set to be smaller than the diameterof the particle of interest, while allowing for some degree of randomparticle movement. In an embodiment where the particle of interest has adiameter of approximately 10 μm, a useful value for 2D_RESIDUAL_DISPLmay be 8 μm. An optimal value of 2D_RESIDUAL_DISPL may for example bebased on analysis of a tradeoff between missing true correlations due toregistration errors, and including false correlations in cases with ahigh particle density or high likelihood of particles being clumpedtogether. The optimal value of 2D_RESIDUAL_DISPL therefore depends onthe particle size.

If step 2282 determines that 2D_RESIDUAL_DISPL is less than 8 μm, orafter blob (ba, bb) is removed from B, compensate chromatic aberration2200 advances to step 2286. Step 2286 is a decision step that determineswhether all correlated blobs (ba, bb) in B have been processed. If not,compensate chromatic aberration 2200 returns to step 2280 to processanother blob (ba, bb), and if so, compensate chromatic aberration 2200returns modified blob list B in step 2290.

FIGS. 30A and 30B are flowcharts of an exemplary subroutine filter lowintensity correlations (BA, B) 2300 that takes an initial blob list BAand a correlated blob list B as input, and modifies correlated blob listB by removing blobs that belong to a distribution other than an expectedmain blob distribution in terms of intensity. Filter low intensitycorrelations 2300 may be performed, for example, by processor 460 ofsystems 100, 100′, taking blob lists BA, discussed above in connectionwith FIGS. 27A through 27C, and correlated blob list BC, discussed abovein connection with FIGS. 28A, 28B, 29A and 29B, as input. Generallyspeaking, filter low intensity correlations 2300 analyzes a histogram ofpeak intensities of a population of blobs (optionally compensating fornon-uniform illumination), to determine a number of populationsdetected, and may discard low-intensity populations that might resultfrom effects such as cross-staining, cross excitation, biologicalproperties, autofluorescence and light scattering. Like compensatechromatic aberration 2200, filter low intensity correlations 2300screens by comparing possible outliers, in this case outlyingpopulations, to a main distribution.

Step 2305 of filter low intensity correlations 2300 receives initialblob list BA and correlated blob list B as input. Step 2310 performs aGaussian distribution fit,

${{f(x)} = {f_{0}{\exp \lbrack {- ( \frac{x - x_{0}}{w} )^{2}} \rbrack}}},$

to TSID values associated with blobs in BA (see, e.g., the explanationof step 2040, FIG. 27B). The Gaussian fit determines parameters f0, x0and w that relate to the height, center point and width of the Gaussianpeak, respectively. Step 2310 also determines the maximum intensity maxyover all intensities in BA, and calculates

$\frac{\max_{y}\lbrack {y_{all}(x)} \rbrack}{f_{0}}.$

Step 2320 is a decision point that determines whether

$\frac{\max_{y}\lbrack {y_{all}(x)} \rbrack}{f_{0}}$

is greater than a parameter threshold, which may be set within a rangeof about 1.5 to 2.0, and is typically 1.7. If not, filter low intensitycorrelations 2300 advances to step 2360, described below. If so, filterlow intensity correlations 2300 advances to step 2330.

Step 2330 calculates a parabolic fit of the usual form ax2+bx+c tointensity data of blob list BA in the range[maxx[yall(x)]:3*maxx[yall(x)]]. Therefore, the range wherein the datais fitted starts at the peak of the intensity distribution from bloblist BA, and extends to three times the peak value. The choice of 3 asthe multiplier that defines the top end of the range is set to clearlyexceed the extent of dim, false event populations in the event that thisis the tallest peak in the histogram; in embodiments, this multipliermight vary within the range of about 2 to 5. A decision step 2340determines whether the parabolic coefficient a is greater than zero. Ifso, the range [maxx[yall(x)]:3*maxx[yall(x)]] fits a parabola that isupward facing, and the vertex of the parabola indicates a demarcationbetween two distinct distributions. That is, the parabolic fit serves tolocate the “valley” between two, possibly overlapping, populations inthe histogram, if two populations exist. The fit range is set to extendacross the valley. Therefore if a>0, filter low intensity correlations2300 advances to step 2350 which removes correlated blobs from B whereinTSID<−b/2a (the parabola vertex). If a<0, or after step 2350, filter lowintensity correlations 2300 advances to step 2355 and returns blob listB.

If filter low intensity correlations 2300 reaches step 2360 as a resultof step 2320, further screening is attempted. Step 2360 calculatesmedian[ycorr(x)] of TSIDs of each blob in correlated blob list B. Adecision step 2370 determines whether x0+2*w (from the Gaussian fitdetermined in step 2310) is greater than median[ycorr(x)] from step2360. If so, the correlated blob distribution does not include asignificant population in addition to the population captured by theGaussian distribution fit, and the population is considered wellbehaved. Therefore if x0+2*w>median[ycorr(x)], filter low intensitycorrelations 2300 advances to step 2394 without further filtering. Thefactor 2 used as a multiplier for w may vary, in embodiments, betweenvalues of about 1.5 and 3.

If step 2370 determines that x0+2*w<median[ycorr(x)], the correlatedblob distribution includes a population with higher values thanpredicted by the Gaussian distribution, and a chance remains that thedistribution includes a peak of false events. In this case, filter lowintensity correlations 2300 advances to step 2380, which againcalculates a parabolic fit of the form ax2+bx+c, this time to datawithin the range of [x0: 3*×0].

A decision step 2340 determines whether the parabolic coefficient a isgreater than zero. If so, the range [x0: 3*×0] fits a parabola that isupward facing, and the vertex of the parabola indicates a demarcationbetween two distinct distributions. Therefore if a>0, filter lowintensity correlations 2300 advances to step 2392 which removescorrelated blobs from B wherein TSID<−b/2a (the parabola vertex). Ifa<0, or after step 2392, filter low intensity correlations 2300 advancesto step 2394 and returns blob list B.

The brightness of the particles of interest, e.g., CD4+T-helper cells,may vary significantly due to both biological variation and measurementrelated effects. In certain embodiments, the camera sensor (e.g., sensor160) has a wide dynamic range, for example 16 bits, to accommodate thisbrightness variation. Alternatively, for a system utilizing a camerasensor with a smaller dynamic range, for example 8 bits, it may not bepossible to find a single exposure time for which all particles areabove the detection limit without some particles reaching saturation.Saturation may affect the apparent properties of a particle of interestin such a way that it is falsely rejected by the particle identificationprocess (e.g., the methods and subroutines called therein, as describedin FIGS. 13-30).

Therefore, in an embodiment, the dynamic range of an 8-bit camera sensoris extended by acquiring multiple images at different exposure times,where the dimmest particles are properly recorded at the longestexposure time and the brightest particles are properly recorded at theshortest exposure time. For example, step 840, described in connectionwith FIG. 13, may consist of acquiring multiple images at differentexposure times. Each of these individual images may be processedaccording to step 845 in FIG. 13, to generate a blob list for eachindividual exposure. Prior to performing step 860 in FIG. 13, thecorrelate source images routine described in FIG. 28 may be used tocorrelate blobs found in more than one exposure. If images are acquiredat only two different exposure times, blob lists from each of these twoexposure times may be propagated through the correlate source imagesroutine, leading to a single correlated blob list. Alternatively, ifimages are acquired at more than two different exposure times, two ofthe exposures may be propagated through the correlate source imagesroutine, leading to a single correlated blob list, which may then bepropagated through the correlate source images routine together with theblob list associated with another exposure time. A loop defined therebymay continue until all exposures for a given light source have beenincorporated in the correlation. Blobs found in only one exposure may bescaled to a common exposure time and placed in a blob list for furtherprocessing. For blobs found in more than one exposure, the brightestoccurrence of the blob where all pixel intensities within the blob areless than or equal to 250 may be scaled to the same common exposure timeand placed in the same blob list. This blob list may then be furtherprocessed as outlined in FIG. 13, beginning with step 860.

In another embodiment, the dynamic range of an 8-bit camera sensor isextended by, in step 840, acquiring multiple images at a constantexposure time set such that no particles of interest are saturated.Prior to performing step 860 in FIG. 13, all these images may be addedpixel by pixel to provide a single, saturation free image of greaterthan 8 bits resolution.

III. Fluidic Features and Methods

In this section, methods and devices for reliably performing passivecontinuous flow in a fluidic channel are described. One method anddevice: (1) utilizes gravity to provide driving pressure; (2) starts andstops liquid flow in a controlled manner; and (3) delivers knownquantities of liquid into the channel. Certain embodiments describedherein further provide continuous flow of a known liquid volume througha channel, with flow terminating before the channel is completelydrained of liquid. As disclosed herein, this effect may achieved by thefollowing process, beginning with filling an inlet port with a knownvolume. Pressure-driven flow due to gravity and surface tension movesthe liquid through a channel to an outlet port. Introduction of awicking pad located near the outlet port absorbs the liquid and ensuresthat flow continues until all the liquid in the inlet port has enteredthe channel. Proper separation of the wicking pad from the outlet port,design of outlet port geometry, and control of solid-liquid-gas surfacetension ensures that flow terminates before the channel is drained ofliquid. The wicking pad further prevents backflow of liquid through theoutlet port into the channel.

The term “surface tension” is used herein in relation to the surfaceenergies of the solid-liquid, liquid-gas, and solid-gas interfacesassociated with the fluidic cartridge. Surface tension or surface energyimpacts the ability of a liquid to wet a solid surface, characterized bya liquid-solid-gas interface. In the present disclosure, exemplarysolids include plastics and plastics with modified surface properties.Exemplary liquids include aqueous solutions, including aqueous solutionswith surface tensions modified by surface active components such assurfactants or amphiphilic molecules. An exemplary gas is air.

FIG. 31 shows a cross-sectional view of a fluidic cartridge 2400, inaccordance with an embodiment. Fluidic cartridge 2400 includes a casing2410 defining a channel 2412 with an inlet port 2414 and an outlet port2416. Casing 2410 may be formed as a single piece or separate piecesthat cooperate to define channel 2412, inlet port 2414 and outlet port2416. For example, casing 2410 may be formed by an injection moldingprocess. As an alternative, casing 2410 may be formed from a combinationof a lower substrate, defining the bottom of channel 2412 and an uppercomponent defining inlet port 2414 and top of channel 2412 connectinginlet port 2414 with outlet port 2416.

For applications, such as in-vitro diagnostics, a liquid 2420 (such asan aqueous solution) may be introduced into channel 2412 at inlet port2414 of fluidic cartridge 2400, as shown in FIG. 32. Due tocharacteristics such as height differences in the fluidic columnsbetween inlet port 2414 and outlet port 2416, a differential pressureexists therebetween that drives liquid 2420 to flow from inlet port 2414to outlet port 2416 in a direction indicated by arrows 2422 and 2424. Ifchannel 2412 has not previously been filled with liquid, capillaryforces due to surface tension may also contribute to moving liquid 2420through channel 2412.

Depending on outlet port 2416 geometry (e.g., diameter and shape) andsurface tension associated with the liquid, solid cartridge material,and gas (typically air), outlet port 2416 acts as a capillary valve witha characteristic burst pressure. Referring to FIG. 33, liquid 2420 flowsthrough the channel then stops at a height hB 2430 as determined by thecapillary valve burst pressure at outlet port 2416.

Once surface tension at outlet port 2416 is overcome by the pressureexerted by liquid 2420 at outlet 2416, liquid 2420 begins to flow out ofoutlet port 2416, as shown in FIG. 34. That is, the difference betweenthe fluid pressure and ambient pressure exceeds a burst pressure,overcoming the surface tension at outlet port 2416. Consequently, liquidflows continuously through channel 2412 until the level of liquid 2420at inlet port 2414 drops to equilibrium level hE 2432 (indicated bydouble arrows), which is lower than first level 2430. At this point,flow ceases because the pressures due to surface tension forces andgravity are balanced between inlet port 2414 and outlet port 2416. Forthe small dimensional sizes of channel 2412 in applications of interest(e.g., on the order of millimeters to tens of millimeters), thegravity-induced forces are comparable in magnitude to surface tensionforces.

In one embodiment, a tilt may be introduced to the fluidic cartridge soas to alter the pressure differential between the inlet port and theoutlet port. As shown in FIG. 35, a tilted cartridge 2440 includescomponents similar to those of previously-described fluidic cartridge2400. In contrast to the embodiment illustrated in FIGS. 31-33, tiltedcartridge 2440 is tilted from a level orientation by an angle φ suchthat the pressure differential at outlet port 2416 is greater than thoseshown in FIGS. 32 and 33. In effect, the column height of inlet port2414 is increased without requiring additional liquid volume, and thusthe gravity-induced pressure is increased over that of the levelorientation. As a result, burst pressure is more easily attained, andliquid 2420 empties to a relatively lower liquid level in tiltedcartridge 2440 as compared to level, fluidic cartridge 2400.

Regardless of the specific configuration used (e.g., level cartridge2400 or tilted cartridge 2440), a fluidic column builds up at outletport 2416 such that at some point liquid flow stops when the pressure atthe outlet port balances the pressure at the inlet port. This conditiondoes not always guarantee that all of the liquid in the inlet port 2414flows through channel 2412 to outlet port 2416. One way to maintainliquid flow through channel 2412 is to introduce a wicking pad, whichessentially acts as a reservoir for absorbing liquid therein. As will beexplained below, the wicking pad acts to reduce the column height of theoutlet port such that liquid flow is maintained. FIG. 36 is across-sectional view of a fluidic cartridge 2450 including a wicking pad2452, in accordance with an embodiment.

As shown in FIG. 37, liquid 2460 may be inserted into fluidic cartridge2450 such that, at inlet port 2414, liquid 2460 reaches a level 2462while liquid 2460 is kept within outlet port 2416 by surface tension.When the burst pressure is exceeded, as shown in FIG. 38, liquid 2460flows out from outlet 2416 in a manner dependent on the liquid-solid-gassurface tension and solid surface geometry. As the liquid column atoutlet 2416 expands, it eventually makes contact with wicking pad 2452,which quickly absorbs liquid 2460. The strong capillary forces ofwicking pad 2452 absorb liquid 2460 at a rate faster than the rate atwhich channel 2412 can supply liquid 2460 to outlet port 2416. Due tothis rate difference, the liquid column height at outlet 2416 is rapidlydecreased. After absorption by wicking pad 2452, the liquid columnheight at outlet 2416 is decreased and then subsequently replenished byflow through channel 2412. Provided that the column height 2470 of inlet2414 provides enough pressure for liquid 2460 to repeatedly overcome theburst pressure and re-contact wicking pad 2452, back pressure from theliquid column of outlet 2416 is avoided and continuous flow occurs inchannel 2412. Flow through channel 2412 is maintained until the liquidcolumn height 2470 of inlet 2414 drops such that there is insufficientpressure to overcome the burst pressure of outlet 2416. Careful choiceof material surface energies, tilt angle, and liquid column heightsenables the entire volume of liquid 2460 in inlet 2414 to be completelyemptied through channel 2412. In this manner, prescribed amounts ofliquid 2460 can be flowed from inlet port 2414 through channel 2412,despite large surface tensions from dimensionally small fluidicsgeometries that might be encountered in a diagnostic device.

Certain embodiments require that the liquid remain in the channel at alltimes during liquid flow and after the inlet has emptied. For instance,an in-vitro diagnostic may require the biological sample in the liquidto incubate in the channel for a period of time so as to allow thesample to chemically react with reagents that are immobilized to thechannel surface. Capillary pressures obtained by wicking pad 2452 can belarge enough to pull liquid 2460 from channel 2412 in an unrestrained oruncontrollable manner, causing channel 2412 to go dry or be filled withdetrimental gas bubbles. Liquid flow from outlet port 2416 to wickingpad 2452 is affected by a number of factors: absorbance properties ofwicking pad 2452 (determined by material composition), geometricalplacement of wicking pad 2452 with respect to outlet 2416, the physicalgeometry of cartridge features like outlet and inlet ports 2414 and2416, and the surface energies of cartridge materials and liquids(determined by material composition, surface treatments, andtime-dependent surface adsorption). One or more of these properties canbe optimized for desired performance. For instance, surface energiesaround outlet port 2416 can be modified by plasma treatment to promotewetting of the solid material by liquid 2460.

In an embodiment, a small gap 2464 is introduced between wicking pad2452 and outlet port 2416 to prevent draining of channel 2412 (see FIG.37). When the rate of liquid 2460 from outlet port 2416 is less thanabsorbance rate of wicking pad 2452 (such as happens when the inlet portempties), surface tension forces in gap 2464 around outlet port 2416“break” the liquid flow to wicking pad 2452. To restore flow, inlet 2414can be filled with sufficient liquid 2460 so that once again the inletpressure exceeds the burst pressure. Flow then resumes as wicking pad2452 once again absorbs excess liquid 2460 from outlet port 2416. Inthis manner, flow can be started and stopped multiple times in acontrolled manner without draining channel 2412 completely of liquid2460. A key aspect of the embodiment is that wicking pad 2452 does notactively pump liquid 2460 through channel 2412, but only acts areservoir to store excess liquids. Gravity provides pressure-driven flowthrough cartridge channel 2412.

An embodiment also employs the use of ridge or rail features at theoutlet port to directionally steer the liquid to the wicking pad.Surface tension forces associated with the sharp corners of the railpreferentially direct the liquid along the rail towards the wicking padin a more controlled manner. FIG. 39 is a cross-sectional view of afluidic cartridge 2480, including a combination of a wicking pad 2482and a rail 2484, in accordance with an embodiment.

FIGS. 40-42 illustrate a process of liquid flow through fluidiccartridge 2480, in accordance with an embodiment. FIG. 40 shows fluidiccartridge 2480 with a liquid 2486 inserted therein such that, initially,liquid 2486 is at a first level 2488 (indicated by a double-headedarrow) at inlet port 2414. When liquid 2486 contacts rail 2484, aportion of liquid 2486 is drawn along rail 2484 by capillary action 2490(indicated by an arrow). Once this portion of liquid 2486 reacheswicking pad 2482, that portion of liquid 2486 immediately in contactwith rail 2484 is drawn into wicking pad 2482 by capillary action 2494(indicated by an arrow), as shown in FIG. 41. Consequently, the level ofliquid 2486 at inlet port 2414 drops incrementally to a second level2492 (indicated by a double-headed arrow). Then, due to a combination ofthe pressure exerted by liquid 2486 and ambient pressure, the processillustrated in FIGS. 40 and 41 is repeated, as liquid level at inletport 2414 drops to a third level 2496 (indicated by a double-headedarrow) and another portion of liquid 2486 is drawn along rail 2484 bycapillary action 2498 (indicated by an arrow), as shown in FIG. 42.

FIG. 43 shows an exploded view of an exemplary cartridge 2500 includinga wicking pad 2520 and rails 2535, in accordance with an embodiment.Cartridge 2500 includes a lid 2510 and a wicking pad 2520, both of whichfit over an upper component 2530. Upper component 2530 defines an inletport 2532, a pair of rails 2535, and an outlet port 2538. Uppercomponent 2530 is attached via an adhesive gasket 2540 to a planarwaveguide arrangement 2550, shown here with a plurality of a microarrayof protein “spots” 2560 printed thereon. FIG. 44 further shows uppercomponent 2530 in combination with wicking pad 2520. As shown in FIG.44, wicking pad 2520 fits around inlet port 2532 and outlet port 2538such that the combination of features functions to provide the flowcontrol mechanism described in FIGS. 39-42.

IV. Cartridge Features and Methods

This section is divided into the following subsections: Cartridge andLid Visual and Tactile Features; Uniform Dried Reagent Placement inInlet Port; Exemplary Performance of Uniform Dried Reagent Placement inInlet Port; Demonstrations of Uniform and Nonuniform Staining in DriedReagent Cartridges; Sample Hold and Release Cartridge; ExemplaryPerformance of Sample Hold and Release Cartridge; andCartridge/Instrument Control Features.

Cartridge and Lid Visual and Tactile Features

FIGS. 45 and 46 illustrate a cartridge 2600 for acquiring and/orprocessing a sample, in an isometric view; FIGS. 47 and 48 showcross-sectional illustrations of cartridge 2600. Cartridge 2600 may beutilized, for example, as a cartridge 130 or 130′ of FIG. 2 or 8.Cartridge 2600 includes a cartridge body 2610 and a cartridge lid 2660.FIGS. 45 and 47 show lid 2660 in an open position relative to body 2610,while FIGS. 46 and 48 show lid 2660 in a closed position. Lid 2660slides within a channel 2640 formed by body 2610. Lid 2660 includes aflange 2670 at a leading edge thereof, that is adapted for capture by acapture feature 2630 of cartridge body 2610, thus forming a lockingmechanism, as discussed below.

When lid 2660 is in the open position shown in FIGS. 45 and 47, an inletport 2620 is exposed. Inlet port 2620 has a volume capacity that isgreater than the volume of liquid required for proper operation ofcartridge 2600 in systems 100, 100′. Inlet port 2620, and other featuresof cartridge 2600 that are exposed to the sample, are treated (e.g.,with plasma) to make them hydrophilic such that the sample is drawnthrough a fluidic path of cartridge 2600. Inlet port 2620 forms an innerregion 2625 that is sized such that its hydrophilic surfaces provide aninterfacial tension that exceeds the force of gravity (for a sample of avolume up to a maximum volume that exceeds the required sample volume aswell as the volume of a typical finger stick). This feature enablesacquisition of such sample by simply inverting cartridge 2600 andplacing inlet port 2620 onto a sample droplet, such as a blood dropleton an upturned finger. Alternatively, the sample droplet can becontacted with cartridge inlet port 2620 while the cartridge is on asurface such as a table top. Alternatively, the sample can transferredinto port 2620 using a transfer devices such as a transfer pipette orother dedicated device (e.g., DIFF-SAFE® blood tube adapter).

Inner region 2625 of inlet port 2620 connects with a fluidic channelthat forms a detection region 2700 that extends for a distance down thelength of the cartridge, providing multiple fields of view for imagingthereof. Downstream of detection region 2700, the fluidic channelconnects with a vent 2690. Vent 2690 has a small channel cross sectionsuch that the expansion at the outlet of vent 2690 forms a capillarygate, thereby stopping flow of the fluid sample. Alternatively, vent2690 may be configured with a cross section much larger than that offluid channel such that the expansion at the inlet to vent 2690 willresult in a capillary gate.

FIGS. 45 and 47 also show bumps 2650 that provide resistance to lid 2660at a known location as lid 2660 slides along channel 2640. Lid 2660 maybe of a flexible material such that it can slide over bumps 2650 whileproviding resistance and tactile feedback. Bumps 2650 serve twofunctions: (1) they discourage accidental locking of lid 2660 into thelocked position (described below) by a user or during shipping andhandling; and (2) bumps 2650 provide a reference location for a window2680 formed in lid 2660. When flange 2670 abuts bumps 2650, as shown inFIGS. 45 and 47, a window 2680 is positioned over a terminal region ofthe fluidic path of cartridge 2600. When a sample (e.g., blood) isloaded into inlet port 2620, the visual appearance of the sample underwindow 2680 indicates that sufficient volume has been supplied to enableanalysis. Upon seeing the sample under window 2680, a user of cartridge2600 can push lid 2660 such that flange 2670 rides over bumps 2650 andcovers inlet port 2620, until capture feature 2630 captures flange 2670.Lid 2660 may be of a flexible material and, prior to capturing flange2670, lid 2660 may be in a stressed state, such that upon flange 2670being captured by capture feature 2630, lid 2660 changes to a lessstressed state. This irreversibly closes lid 2660 over inlet port 2620so that potentially biohazardous samples can be handled safely, andprevents reuse of cartridge 2600. In the embodiment shown in FIGS. 45through 48, capture feature 2630 includes a ridge that rides up overflange 2670 as lid 2660 moves into the closed position, generating adownward force thereon. When a trailing edge of flange 2670 reaches aleading edge of cartridge body 2610, flange 2670 is captured, with theridge forming a locking mechanism that prevents flange 2670 from beingeasily dislodged. Other embodiments may have different physical featureson either a cartridge body or lid that perform the functions of coveringan inlet port and locking the lid to the cartridge, making the cartridgesafe to handle even with a biohazardous substance therein.

In its closed position, lid 2660 may function to reduce evaporation fromcartridge 2600, which may result in an extension of the time allowed topass between sample loading and readout of cartridge 2600 using, e.g.,systems 100, 100′ shown in FIGS. 2 and 8. Another embodiment of acartridge lid allows for addition of fluid to cartridge 2600 to enhancethe humidity inside cartridge 2600, thereby reducing evaporation. Yetanother embodiment of a cartridge lid has sealing functionality as wellas features to allow for fluid addition to cartridge 2600.

FIG. 49 shows an exploded view of a cartridge 2600′ that may be utilizedas a cartridge 130 or 130′, FIG. 2 or 8, or cartridge 2600, FIGS. 45-48.Cartridge 2600′ includes a top cartridge element 2710, a bottomcartridge element 2720, a lid 2730 and a label 2740. Top cartridgeelement 2710 has an indentation feature (not visible in FIG. 49) thatforms a fluid channel when connected, for instance by laser welding, tobottom cartridge element 2720.

FIG. 50 shows an exploded view of a cartridge 2600″ that may be utilizedas a cartridge 130 or 130′, FIG. 2 or 8, or cartridge 2600, FIGS. 45-48.Cartridge 2600″ includes a top cartridge element 2760 and a bottomcartridge element 2770 with a gasket 2765 between. Inner surfaces of topcartridge element 2760 and bottom cartridge element 2770 are planarsurfaces, such that thickness of gasket 2765 may be tightly controlledto set channel height 385, FIG. 6. Alternatively, one or both of topcartridge element 2760 and bottom cartridge element 2770 may include astandoff that defines channel height 385. Cartridge 2600″ also includesa lid 2780 and a label 2790. In cartridge 2600″, top cartridge element2760 does not include an indentation feature. Instead, gasket 2765 formsan aperture 2767 that, when gasket 2765 connects top cartridge element2760 with bottom cartridge element 2770, forms a fluid channel.

Lid 2730 of cartridge 2600′ (shown in FIG. 49) or a portion thereof and,equivalently, lid 2780 of cartridge 2600″ (shown in FIG. 50) or aportion thereof may be manufactured of an optical grade, clear materialto allow for loss free and distortion free illumination from above, iffor instance used in systems 100, 100′ shown in FIG. 3. If lid 2730 orlid 2780 includes a label thereon, the label's impact on the opticalsystem should be considered. For example, any label through whichillumination beams would pass may have to be formed of a material ofoptical quality and controlled thickness so that placement of theillumination beam at the measurement field would be controllable. Forthis reason, it may be advantageous for the label not to be placed overthe detection region, or to form apertures or cutouts in the label tokeep the label out of the way of the illumination beams. In anotherembodiment, illumination and detection may be performed from below, inwhich case lid 2730 of cartridge 2600′ and lid 2780 of cartridge 2600″may be opaque, of lesser than optical quality, and/or include features,labels, etc. over the detection region.

Uniform Dried Reagent Placement in Inlet Port

A cartridge embodiment is now described that utilizes engineered driedreagent methods to deliver accurate analyte detection directly from asmall volume liquid sample, e.g., a volume of about 10 microliters.Examples of analytes include particle analytes such as CD4+T-helpercells, other cell types, bacteria, viruses, fungi, protozoa, and plantcells, and non-particle analytes such as proteins, peptides, prions,antibodies, micro RNAs, nucleic acids, and sugars. FIGS. 51A and 51Bschematically illustrate a cartridge 2830 that includes a planar plasticsubstrate 2810 and a plastic upper housing component 2820. FIG. 51A is aplan view of cartridge 2830 looking upwards from a line 51A-51A in FIG.52, while FIG. 51B is a cross-sectional view taken at line 51B-51B inFIG. 51A; features in FIGS. 51A and 51B are not necessarily drawn toscale. Although the examples described here are generally in the contextof whole blood analysis, the cartridge with dried reagent has utilityfor other sample types and is not limited to use with whole bloodsamples.

Planar plastic substrate 2810 and plastic upper housing component 2820are formed of cyclic olefin polymer (COP), although other plastics(e.g., polystyrene) have been successfully used in the sameconfiguration. Planar plastic substrate 2810 has approximate dimensionsof 1 mm×20 mm×75 mm. Cartridge 2830 features a “bulls-eye” inlet port2835 that has an outer region 2840 adjoining an inner region 2845 thatmay be D-shaped, as shown. Inner region 2845 may also be shapeddifferently from the D-shape shown, in particular an O-shape has beensuccessfully demonstrated. Inner region 2845 connects with a fluidicchannel 2850, leading to a vent opening 2860. A detection region 2870forms part of fluidic channel 2850, as shown. In the embodiment shown inFIGS. 51A and 51B, cartridge 2830 is manufactured by aligning substrate2810 and upper housing component 2820 with an adhesive gasket 2880 thatsets height of fluidic channel 2850; in an embodiment, gasket 2880 isapproximately 35 μm thick. Once aligned, substrate 2810, upper housingcomponent 2820 and gasket 2880 are pressed together. In alternativeembodiments, a plastic substrate and an upper housing component may belaser welded together; in such embodiments one or the other of thesubstrate and the upper housing component may have a channel featuremolded therein to form the fluidic channel.

Part or all of inner surfaces 2812, 2822 of planar plastic substrate2810 and plastic upper housing component 2820 respectively may betreated with an argon/oxygen plasma to render these surfaceshydrophilic. A hydrophilic surface promotes uniform capillary drivenflow in the final assembled cartridge 2830. Experiments have shown thatimmediately following plasma treatment, a water contact angle ofsurfaces 2812, 2822 is less than 10 degrees; relaxation in dry airresults in a stable contact angle of approximately 30 degrees.

In the embodiment shown in FIGS. 51A and 51B, a liquid reagent isdeposited as an array of droplets 2847 on surface 2812; FIGS. 51A and51B schematically show droplets 2847 immediately after deposition. Sizeand arrangement of droplets 2847 are controlled such that the spots areclose enough to spread and merge before drying, to form a relativelyuniform coating, as described below with respect to FIGS. 52A and 52B.

FIGS. 52A and 52B schematically illustrate cartridge 2830′ that resultsfrom cartridge 2830 after sufficient time for droplets 2847 to spread,merge and dry, forming dried reagent coating 2848. Temperature andhumidity control may be balanced such that droplets 2847 have justenough time to spread and merge to form coating 2848 before the reagentcompletely dries. This is advantageous because the reagent optimallyspreads into a uniform coating, but remains within inlet port 2845 forgood contact with liquid samples loaded into the inlet port enroute tofluidic channel 2850. It may be advantageous to further process thedried reagent coating by freeze-drying or lyophilization, to promoteuniform reagent-sample interactions across the detection region in thefluidic channel.

FIGS. 53A and 53B schematically illustrate an alternative cartridge 2930that has many features common to cartridges 2830, 2830′ but has aD-shaped dried reagent coating 2940 that matches the shape of innerregion 2845 of inlet port 2835. FIGS. 54A and 54B schematicallyillustrate an alternative cartridge 2950 that has many features commonto cartridges 2830, 2830′, 2930 but has a dried reagent coating 2960that is located within fluidic channel 2850, downstream of inlet port2835.

Exemplary Performance of Uniform Dried Reagent Placement in Inlet Port

This example provides a demonstration of using engineered dried reagentmethods to deliver a useful, single-step assay cartridge that deliversan accurate CD4 T-cell count directly from a small volume of wholeblood, e.g., a volume of 10 microliters. A liquid reagent formulationcontaining 1% sucrose, 0.2% PEG8000, 1% bovine serum albumin(mass/volume %'s), phycoerythrin-labeled anti-CD3 monoclonal antibody(0.4 μg/mL), Alexa647-labeled anti-CD4 monoclonal antibody (0.4 μg/mL),and 25 mM HEPES buffer was prepared. A robotic non-contactmicro-dispenser equipped with a pressure driven solenoid valve printhead (Biojet, Bio-Dot, Inc.) was used to deposit the liquid reagentformulation into an array of droplets 2847 in a pre-determined patternon substrates 2810. Individual droplets 2847 were 25 nanoliters involume, positioned with a center-to-center spacing of 0.5 millimeters ina 62-spot pattern that approximated the D-shaped inlet opening 2845. Themicro-dispenser included a temperature and humidity-controlledenclosure. Deposition of the 62-spot pattern was performed at 21 to 24°C. and 65% relative humidity. The print pattern is conceptually shown inFIG. 51A. At this temperature, humidity, and substrate surface energy,the 25 nanoliter droplets 2847 simultaneously spread while rapidlydrying. Droplets 2847 contacted and merged while quickly stopping in aninterim “dry” deposition coating 2848 with relative uniformity acrossthe D-geometry, as schematically represented in FIGS. 52A, 52B.Substrates with the 62-spot printed array were processed in a 9 hourlyophilization protocol using a SP Scientific Virtis Vantage Plus ELlyophilizer. After lyophilization, the substrates were aligned withcustom tooling and bonded together with a gasket 2880 approximately 35micrometers thick. Gasket 2880 was manufactured with a cutout thatdefines a fluidic channel and with two liners that are removed as partof the assembly process. The cartridge components (e.g., substrates 2810with coatings 2848, gasket 2880 and upper housing component 2820) wereassembled using a pneumatic press to form cartridges 2830′. Afterassembly, cartridges 2830′ were packaged in heat sealed barrier pouchesuntil use.

Each assembled cartridge 2830′ was placed on a flat surface andapproximately 10 microliters of whole blood was added via transfer pipetto inlet port 2840 containing dried reagent coating 2848. This stepinitiated rehydration of dried reagent 2848. When the blood contactedthe entrance to fluidic channel 2850, it was drawn in by capillaryforces. Blood-filled cartridges 2830′ were allowed to incubate on abench top at ambient temperature (˜21° C.) for 20 minutes. AbsoluteCD4+T cell counts were generated using a reader instrument as describedabove (e.g., system 100′, FIG. 8, utilizing imaging and analysis methodssuch as described above). Results comparing the CD4 count from theassembled cartridges 2830′ to results obtained from a reference flowcytometer are provided in FIG. 55. The count accuracy and precisionshown in FIG. 55 demonstrate utility of dried reagent cartridge 2830′.

Demonstrations of Uniform and Nonuniform Staining in Dried ReagentCartridges

In this subsection, descriptions and schematics related to the use ofdried reagents integrated in a cartridge are provided. Specifically,schematic representations of uniform and non-uniform sample staining byrehydrated dried reagents are described. A dried reagent coating shouldbe positioned to yield spatially uniform reagent-sample interactionswithin a detection region of a cartridge. Reagent-sample interactionsinclude for example rehydration of a dried reagent and staining of thesample. Because fluid flow is generally laminar in the cartridgesherein, mixing in a width direction of fluidic channels of thesecartridges is minimal (e.g., primarily diffusion). Thus, when a driedreagent layer is nonuniform, the resulting sample staining may benon-uniform across the channel width. This phenomenon may be observed byvisual analysis of fluorescence images in the detection region. Uniformstaining is important because particle counts (e.g., CD4+T-helper cellcounts) may be affected by staining; that is, when a particleidentification system (e.g., systems 100, 100′) counts particles and inparticular counts particles in multiple measurement fields, thestatistical validity of the counts and variation among the counts permeasurement field will be adversely affected if staining is nonuniform.

When dried reagent deposition is spatially uniform and rehydration ratesare been properly designed into a lyophilization formulation, a liquidsample will stain uniformly throughout a detection region. Uniformstaining was observed, for example, in the example discussed above.Fluorescence images in the detection region were analyzed and uniformstaining was confirmed by visual analysis of sets of digital images, andcount accuracy, illustrated in FIG. 55, matched results obtained by flowcytometry.

FIGS. 56A and 56B schematically illustrate cartridge 2830″ thatrepresents cartridge 2830 after addition of a blood sample. In FIGS. 56Aand 56B, fluidic channel 2850 and detection region 2870 therein areshown containing a sample 2891 that is uniformly stained.

FIGS. 57A and 57B schematically illustrate a cartridge 2970 afteraddition of a blood sample 2892. Cartridge 2970 includes the sameplastic and gasket parts as cartridges 2830, 2830′ and 2830″ butincludes a dried reagent region 2941 that is poorly formed, and coversonly one side of inner region 2845 of inlet port 2835, as shown.Consequently, in FIGS. 57A and 57B, fluidic channel 2850 includingdetection region 2870 are shown as containing a sample that isnonuniformly stained; region 2893 of the sample has a much smalleramount of reagent than region 2894. This undesirable effect, and similareffects described in connection with FIGS. 58A, 58B, 59A and 59B, may bedetectable by the techniques in the Cartridge Instrument/Controlsubsection below, described in connection with FIGS. 64-66. A similareffect may result from a dried reagent layer that, although spanning theinlet port opening, is non-uniform in thickness.

FIGS. 58A and 58B schematically illustrate a cartridge 2972 afteraddition of a blood sample. Cartridge 2972 includes the same plastic andgasket parts as cartridges 2830, 2830′ and 2830″ but includes a driedreagent region 2942 in which reagent rehydration is too rapid withrespect to fluid flow, or does not contain enough dried reagent. Rapidrehydration leads to complete dissolution of reagent into a leading edgeof the liquid sample. In this case, the trailing volume does not getproperly stained. The result is non-uniform staining down the length offluidic channel 2850. Consequently, in FIGS. 58A and 58B, fluidicchannel 2850 including detection region 2870 are shown as containing asample that is nonuniformly stained; region 2895 of the sample has asmaller amount of reagent than region 2896. In embodiments, rehydrationrate may be slowed by additives such as sugars.

FIGS. 59A and 59B schematically illustrate a cartridge 2973 afteraddition of a blood sample. Cartridge 2973 includes the same plastic andgasket parts as cartridges 2830, 2830′ and 2830″ but includes a driedreagent region 2943 in which reagent rehydration is too slow withrespect to fluid flow. Rapid rehydration leads to complete dissolutionof reagent into a leading edge of the liquid sample. In this case, theleading edge of liquid sample moves into the fluidic channel withoutpicking up stain. Again, the result is non-uniform staining down thelength of fluidic channel 2850. Consequently, in FIGS. 59A and 59B,fluidic channel 2850 including detection region 2870 are shown ascontaining a sample that is nonuniformly stained; region 2897 of thesample has a smaller amount of reagent than region 2898.

Sample Hold and Release Cartridge

In this subsection, cartridge features and methods of their use forselectively holding and releasing fluid flow in a cartridge aredisclosed. Such features are useful because they facilitate control ofincubation time of a sample within a cartridge, for example to controlrehydration of a reagent and/or exposure of the sample to a reagent.That is, holding a liquid sample in the inlet port may be advantageousin certain applications in which a reagent dissolution step is required.The hold time can be selected for optimum dissolution/rehydration. Oneway to provide such control is to provide a frangible surface connectedwith a fluidic path such that before the surface is broken, air trappedin the fluidic path stops the advancement of fluid, but after thesurface is broken, the air may escape such that capillary forces candraw the fluid towards the broken surface. Upon addition of a liquidsample, the liquid “seals” the slot-shaped entrance to the fluidicchannel and there is no path for the air in the channel to escape. As aresult, the sample sits in the inlet port without substantively enteringthe fluidic channel.

FIGS. 60A and 60B are schematic representations of a liquid sample 2991being held in inlet port 2835 of a cartridge 2990. Cartridge 2990includes the same plastic and gasket parts as cartridges 2830, 2830′ and2830″ but includes a frangible seal 2865 covering vent 2860. Frangibleseal 2865 may be for example an adhesive vent cover. After auser-determined time, frangible seal 2865 is punctured. Capillary forcesdraw liquid sample 2991 into fluidic channel 2850, as shown in cartridge2990′, FIGS. 61A and 61B. Displaced air escapes through vent 2860.

FIGS. 62A and 62B are cross-sectional illustrations showing a cartridge3000 that has features to hold and release a liquid sample into afluidic channel. Cartridge 3000 includes a cartridge body 3010 and acartridge lid 3060. FIG. 62A shows lid 3060 in a first, open positionrelative to body 3010, while FIG. 62B shows lid 3060 in a second, closedposition. Lid 3060 slides within a channel formed by body 3010. Lid 3060includes a flange 3070 at a leading edge thereof, that is adapted forcapture by a capture feature 3030 of cartridge body 3010, as discussedabove in connection with FIGS. 45-48.

When lid 3060 is in the open position shown in FIG. 62A, an inlet port3020 is exposed. Inlet port 3020 has a volume capacity that is greaterthan the volume of whole blood required for proper operation ofcartridge 3000 in systems 100, 100′. Inlet port 3020, and other featuresof cartridge 3000 that are exposed to a sample, are treated (e.g., withplasma) to make them hydrophilic. Inner region 3025 of inlet port 3020connects with a fluidic channel 3100 that extends down the length of thecartridge, providing multiple fields of view for imaging thereof.Fluidic channel 3100 connects with a vent 3090 that is covered with anunbroken frangible seal 3110 in FIG. 62A. Lid 3060 includes a protrusion3120 on an underside thereof, which breaks frangible seal 3110 when lid3060 is moved to a closed position, as discussed below.

The hydrophilic surfaces of inlet port 3020 generate a capillary forcethat enables acquisition of a sample by simply inverting cartridge 3000and placing inlet port 3020 onto a blood droplet on an upturned finger.Alternatively, the blood droplet can contact cartridge inlet port 3020while the cartridge is on a surface such as a table top. Alternatively,the sample can transferred into port 3020 using a transfer devices suchas a transfer pipette or other dedicated device (e.g., DIFF-SAFE® bloodtube adapter). If trapped air within fluidic channel 3100 did not stopthe sample, the hydrophilic surfaces and small surface geometry offluidic channel 3100 will continue to draw the sample through fluidicchannel 3100. However, in the lid position shown in FIG. 62A, the backpressure of trapped air within fluidic channel 3100, blocked byfrangible seal 3110, keeps the sample from proceeding down fluidicchannel 3100.

After a sample is loaded into inlet port 3020 while cartridge 3000 haslid 3060 in the first or open position shown in FIG. 62A, lid 3060 canbe moved to the second or closed position shown in FIG. 62B. In closinglid 3060, the features of cartridge 3000 cooperate to produce severaluseful outcomes: (1) Lid 3060 covers inlet port 3020, sealing the samplewithin cartridge 3000 for safety purposes, since the sample may bebiohazardous. (2) Capture feature 3030 captures flange 3070, preventingaccidental reopening of cartridge 3000. This is a safety feature andprevents accidental re-use of cartridge 3000, which may lead tounreliable results since the initial sample would have dissolved andmixed with reagents therein. (3) Protrusion 3120 pierces frangible seal3110, resulting in perforated frangible seal 3110′. Perforated frangibleseal 3110′ is not airtight and allows air to escape from fluidic channel3100, so that capillary forces draw the sample from inlet port 3020 intofluidic channel 3100. This positions the sample over a detection region(not labeled in FIGS. 62A and 62B) for imaging and particle counting, asdescribed above.

Exemplary Performance of Sample Hold and Release Cartridge

Cartridges 2990 with integrated dried reagents were prepared andassembled as described in connection with FIGS. 60A, 60B above. Prior toperforming an assay with the cartridge, each vent hole 2860 was sealedwith adhesive tape (Nunc Aluminum Seal Tape), forming a frangible seal2865. By covering vent hole 2860, the fluidic channel became a closedend, effectively hermetically sealed chamber. In this specific example,a 10 microliter whole blood sample was added to each inlet port 2835using a transfer pipet. The blood samples sat in inlet ports 2835without entering fluidic channels 2850. After a ten second hold, eachvent 2860 was opened by manually puncturing each respective frangibleseal 2865. Immediately upon puncturing frangible seal 2865, blood flowedinto each respective fluidic channel 2850. The resulting blood-filledcartridges 2990′ were then allowed to incubate on the bench top atambient temperature (˜21° C.) for 20 minutes. Absolute CD4+T cell countswere generated using the reader instrument described in the presentinvention. Results comparing the CD4 count from cartridges 2990′ toresults obtained from a reference flow cytometer are provided in FIG.63. The achieved count accuracy and precision demonstrate utility ofcartridges 2990, 2990′. Cartridge 3000 implements similar features tothose of cartridges 2990, 2990′ to simplify sample collection andprocessing in a clinical environment (e.g., by facilitating samplecollection, integrating protrusion 3120 for piercing frangible seal 3110into the cartridge lid, and facilitating sealing of the cartridge simplyby closing lid 3060 with respect to cartridge body 3010).

Cartridge/Instrument Control Features

In many applications, it is desirable to incorporate system featuresthat ensure proper operation of an assay protocol, cartridge, andinstrumentation. In this subsection, various embodiments associated withsystem quality controls are described.

The embodiments described here are based on a cartridge (e.g., one ofcartridges 130, 130′, 2500, 2600, 2600′) that incorporates one or morefluidic channels that serve as sample chambers with detection regions.By incorporating inlet and outlet ports, the fluidic channels facilitateperforming sequential fluidic assay steps. In many instances, it isdesirable to know if a fluidic channel has sufficient liquid volume forperforming a particular assay operation.

Detection of a liquid in a certain region, such as the detection regionin a cartridge, may rely on for example, electrical or optical methods.Presence of a fluid may be detected optically by relying on propertiesof the fluid that differ from properties of a material replaced by thefluid, such as air or another liquid or fluid. If the fluid is more orless absorptive at least at a certain wavelength, its presence may bedetected by an absorption measurement. If the fluid contains fluorescentmaterial, its presence may be detected by performing a fluorescencemeasurement. Thus, sample addition, rehydration of dried reagents andproper staining of the sample by the reagents may all be consideredcontrol features for evaluating assay validity. These features areviewable by an imager within one or more measurement fields of acartridge.

In an embodiment, a fluorescence measurement is performed to read outthe results of an assay such as a fluorescent immunoassay or afluorescent immunostaining assay. The assay itself may involveincubating a sample with fluorescent material prior to the sampleentering a detection region of a cartridge. Presence of the sample maybe detected by detecting the fluorescent material utilizing the samefluorescence measurement system that is used for the assay. This methodhas the benefit that it will detect the presence of a required assayreagent and may be configured to determine a value indicative of theamount of fluorescent material present in the detection region. Thisvalue may be used for calibration purposes. In cases where it ispossible for the fluorescent material to populate the region withoutactual sample addition, this method may be utilized exclusively fordetecting presence, and optionally, an amount of fluorescent materialpresent.

It is also possible to deduce sample presence from detecting propertiesof an assay requiring the presence of both sample and assay reagents. Inan embodiment, a cartridge is used to measure certain analytes.Successful detection of at least some of these analytes may be used as ameasure of sample presence, as well as presence of assay reagents andvalidity of the assay. The detection scheme utilized may be the same asthat used to read out actual assay results. In another embodiment, thepresence of sample, and optionally the presence of reagents as well asassay validity, may be deduced from data recorded to determine the assayresults, with no need to perform measurements in addition to those beingdone to perform the assay.

Analytical methods are, for example, used to detect one or more changesin parameters, that can indicate incomplete liquid fill of a fluidicchannel or sample chamber. In one embodiment, a cartridge and readersystem are used to identify and/or count a certain particle type withina sample. By counting the particles in discrete locations (e.g.,measurement fields) in a fluidic channel, count statistics can be usedto identify changes indicative of incomplete channel fill. For example,a sudden change in particle count that exceeds a predetermined amount(e.g., empirically derived) may indicate incomplete channel fill. Inanother embodiment, a large percent coefficient of variation (% CV,defined as the ratio of sample standard deviation to the mean, expressedas %) for a series of measurement fields across a fluidic channel mayindicate incomplete liquid fill. Therefore, in an embodiment, count % CVacross a fluidic channel is compared to a Poisson-limited % CV. A count% CV that exceeds the Poisson-limited % CV by a given amount can beinterpreted as an incomplete channel fill. A demonstration of thisembodiment is now provided.

Example Presence of Sample and Detection Reagent in Cartridge EvaluatedThrough Image Analysis

Tests were performed on a system that included a cartridge configuredfor detection of T-helper cells in a whole blood sample, and aninstrument for identification and counting of the T-helper cells in thecartridge through fluorescence imaging. The cartridge included a fluidicchannel with a detection region, such as the cartridges describedherein. A blood sample was provided; before the blood sample entered thedetection region, the sample was mixed with an immunostain includinganti-CD3 antibodies labeled with Phycoerythrin (PE) and anti-CD4antibodies labeled with Alexa647 (A647). The instrument recorded PEfluorescence images and A647 fluorescence images of twelve measurementfields along the fluidic channel. The images were analyzed using partsor all of the software routine described in FIGS. 13 through 30B. Theinstrument also calculated average fluorescence signal in imagesobtained under illumination from each of two illumination sources (e.g.,illumination sources 200, 300, FIG. 2) and a number of T-helper cellsidentified from the images. T-helper cells have CD3 and CD4 receptors,and therefore produce signal under illumination from each of the twoillumination sources.

These tests demonstrated the use of image analysis to determine thatsufficient sample and/or detection reagent was added to a fluidicchannel in an assay cartridge. In an experiment, three cartridges wereimaged. Cartridge A was known to have a properly filled detectionregion, whereas cartridges B and C were intentionally under-filled suchthat some measurement fields contained no sample. FIGS. 64, 65 and 66show average signal recorded in both fluorescence channels, as well asthe number of T-helper cells for each measurement field, for each of thethree cartridges (both the T-helper cell counts and the fluorescencechannel signals are referred to below as metrics). Measurement fields 1and 12 were the extreme upstream and downstream measurement fields,respectively, in the detection region of each cartridge. When comparingcartridges A, B, and C, it is clear that cartridge A produced relativelyconsistent levels for all three metrics, while cartridges B and Cexhibited a clear drop in all three metrics at a certain point along thelength of the channel. The most distinct drop was exhibited by theT-helper cell count that dropped from a level of 50-100, to a near zerovalue for both cartridges B and C.

Numerous methods may be used to identify sudden value changes as well astheir location. In the present experiment, the presence of a suddenchange in the number of T-helper as a function of channel position wasidentified by calculating the coefficient of variation (% CV) for theT-helper cell count. Cartridges A, B, and C had % CVs of 16%, 61%, and223%, respectively. For comparison, the Poisson limited % CV is 11%,e.g., the expected % CV for a measurement subject only to countingstatistics type errors will average 11%. Clearly, cartridges B and C hadabnormally large % CVs, indicating a partially filled channel.

Further analysis was performed in order to locate determine if theinvestigation revealed only a single, sudden T-helper cell count drop,or if the large % CVs for cartridges B and/or C were caused by highlyvariable T-helper cell counts. For measurement fields 2-12, a relativechange compared to the preceding measurement field was calculated as[count(i)−count(i−1)]/[count averaged over all measurement fields],where i is the measurement field number. The results are shown in Table1 below.

TABLE 1 Relative count change and average PE signal change, compared toprevious measurement field, for T-helper cells counted in cartridges A,B and C Relative change in Relative change in T-helper cell countaverage PE signal Measurement Cartridge Cartridge Cartridge CartridgeCartridge Cartridge field A B C A B C 1 2 −4% −31% 10% 2% −2% −16% 3−37% −20% −151% −5% 6% −18% 4 14% 8% 0% 0% −5% 0% 5 −12% −4% 0% −1% 3%1% 6 −22% −4% 2% 4% −5% 1% 7 27% −27% 2% 0% 0% 4% 8 −14% −6% −4% −1% −8%−4% 9 53% 35% 4% −4% −3% 4% 10 −14% −121% −2% −2% −22% −1% 11 6% −8% −2%5% 0% 1% 12 27% 0% 0% 2% 2% −2%

Since an empty measurement field is expected to lead to a near-zerocount, a partially filled channel was diagnosed by relative changessmaller than −100% and/or greater than +100%. Cartridge A showed no suchchanges, while cartridges B and C showed a relative change smaller than−100 at measurement fields 10 and 3, respectively. It was deduced fromthese results that the detection region of cartridge A was properlyfilled while the detection region of cartridges B and C were filled onlythrough measurement fields 9 and 2, respectively.

A similar method may be applied to the average PE signal to determine acartridge underfill condition. In the case of the PE signal, a criterionthat can be used to determine underfill is a−12% relative change in PEsignal from one measurement field to the next.

Another embodiment of the cartridge is provided describing a device(e.g., cartridge) for analyzing an analyte in a sample. The device mayinclude at least a first substrate, a second substrate, a fluidicchannel, an inlet port, and an outlet port. In one aspect, the firstsubstrate and said second substrate each has an inner surface and anouter surface. The inner surface of the first substrate may form, atleast in part, the lower wall of the fluidic channel, while the innersurface of the second substrate may form, at least in part, the upperwall of the fluidic channel. In another aspect, the fluidic channel isconnected to both the inlet port and the outlet port. In another aspect,the fluidic channel includes at least a reagent region and a detectionregion, and at least a portion of the reagent region is coated with oneor more dried reagents, which contain at least a detection molecule thatbinds the analyte in the sample. In another aspect, the device alsocontains a wicking pad located on the outer surface of the secondsubstrate, and the wicking pad is positioned at a pre-determineddistance from the outlet port. In another aspect, the reagent region islocated between the inlet port and the detection region, such that thesample, when added to the inlet port, passes through the reagent regionbefore entering the detection region. In another aspect, the analytebound with the detection molecule may be detected in the detectionregion.

In another aspect, the one or more dried reagents may form a spatiallyuniform layer at the reagent region. In another aspect, the driedreagent coating may be distributed evenly along the width of the fluidicchannel that is perpendicular to the sample flow path from the inletport to the outlet port. This uniform layer may be formed by depositingliquid reagents onto the reagent region forming a plurality of singlespots, and by allowing the plurality of single spots to merge before theliquid in each single spot evaporates. In another aspect, each of thesingle spots may receive from 1 to 50 nanoliters of liquid reagents, andthe center-to-center spacing of the single spots and the volumedeposited to each spot are collectively controlled to ensure thatdroplet-to-droplet contact occurs following deposition. In anotheraspect, the dried reagent coating may have a rehydration rate andphysical dimension that collectively yield spatially uniformreagent-sample interaction within the detection region. The rehydrationrate of the dried reagent coating may be determined by the reagentformulation and the composition of the sample. In another aspect, thedried reagent may contain an additive, such as sucrose, that slows therehydration rate of the dried reagent coating.

In another aspect, the inlet port may have a volume greater than thevolume of the fluidic channel, which may generate capillary action thatfacilitates movement of the sample from the inlet port to the fluidicchannel. In another aspect, the walls of the inlet port, the walls ofthe fluidic channel, or both may be coated, either entirely or in part,with a hydrophilic layer.

In another aspect, the walls of the inlet port and the walls of thefluidic channel may be rendered hydrophilic by its building material, bythe coating of the hydrophilic layer, or by other treatment of thebuilding material such as plasma treatment, so that they have a watercontact angle of less than 50 degrees, less than 40 degrees, less than30 degrees, or less than 10 degrees.

In another aspect, the cartridge may have an internal tilt relative to alevel orientation, which is sufficient to drive flow of the liquidsample from the inlet port to the outlet port. In another aspect, one,two, and/or three of the factors, namely, the tilt, the capillaryaction, and the wicking pad, may contribute to driving the flow of theliquid sample from the inlet port to the outlet port. In another aspect,the tilt is at an angle between 2 and 45 degrees relative to a levelorientation.

In another aspect, the distance between the wicking pad and the outletport is sufficient to prevent the wicking pad from draining the fluidicchannel. In another aspect, the wicking pad is made of a material havinga wicking rate of between 10 and 200 seconds per 4 centimeters (cm) ofthe material. In another aspect, the wicking pad is made of a materialhaving a certain absorbance rate, wherein the surface tension of theliquid sample emerging from the outlet port breaks the fluidicconnection between the wicking pad and the outlet port when theabsorbance rate exceeds the rate at which the liquid sample emerges fromthe outlet port, thereby preventing further fluidic flow from the outletport to the wicking pad. In another aspect, the distance between thewicking pad and the outlet port is between 1 and 5 mm.

In another aspect, the detection region includes a plurality of capturemolecules bound to the inner surface of the first substrate. In anotheraspect, the plurality of capture molecules are arranged as an arrayincluding at least two reaction sites, each of the at least two reactionsites being formed by depositing a composition onto the inner surface ofthe first substrate, wherein the composition contains at least one ofthe capture molecules. In another aspect, binding of the dried reagentto the analyte in the sample does not prevent binding of the sameanalyte to the plurality of capture molecules. Details of the capturemolecules and the array may be found in U.S. patent application Ser. No.13/233,794 as filed on Sep. 15, 2011, which is incorporated herein byreference in its entirety.

The changes described above, and others, may be made in the particleidentification systems, cartridges and methods described herein withoutdeparting from the scope hereof. It should thus be noted that the mattercontained in the above description or shown in the accompanying drawingsshould be interpreted as illustrative and not in a limiting sense. Thefollowing claims are intended to cover all generic and specific featuresdescribed herein, as well as all statements of the scope of the presentmethod and system, which, as a matter of language, might be said to falltherebetween.

What is claimed is:
 1. A device for analyzing an analyte in a sample,said device comprising a first substrate, a second substrate, a fluidicchannel, an inlet port, and an outlet port, wherein said first substrateand said second substrate each has an inner surface and an outersurface, the inner surface of the first substrate forming, at least inpart, the lower wall of said fluidic channel, the inner surface of saidsecond substrate forming, at least in part, the upper wall of saidfluidic channel, said fluidic channel being connected to said inlet portand said outlet port, and wherein said fluidic channel comprises areagent region and a detection region, at least a portion of the reagentregion being coated with one or more dried reagents, wherein said devicefurther comprises a wicking pad located on the outer surface of saidsecond substrate, said wicking pad being positioned at a pre-determineddistance from said outlet port.
 2. The device of claim 1, wherein thereagent region is located between the inlet port and the detectionregion, such that the sample, when added to the inlet port, passesthrough the reagent region before entering the detection region.
 3. Thedevice of claim 1, wherein the one or more dried reagents comprise adetection molecule that binds the analyte in the sample.
 4. The deviceof claim 3, wherein the analyte bound with the detection molecule isdetected in the detection region.
 5. The device of claim 1, wherein theone or more dried reagents form a spatially uniform layer at the reagentregion.
 6. The device of claim 1, wherein the dried reagent coating isdistributed evenly along the width of the fluidic channel that isperpendicular to the sample flow path from the inlet port to the outletport.
 7. The device of claim 5, wherein the uniform layer is formed bydepositing liquid reagents onto the reagent region forming a pluralityof single spots, wherein the plurality of single spots merge before theliquid in each single spot evaporates thereby forming the uniform layerof dried reagents.
 8. The device of claim 7, wherein each of the singlespots receives 1-50 nanoliters of liquid reagents in volume, wherein thecenter-to-center spacing of the single spots and the volume deposited toeach spot collectively ensure droplet-to-droplet contact followingdeposition.
 9. The device of claim 1, wherein the dried reagent coatinghas a rehydration rate and physical dimension that collectively yieldspatially uniform reagent-sample interaction within the detectionregion.
 10. The device of claim 1, wherein rehydration rate of the driedreagent coating is determined by the reagent formulation and thecomposition of the sample.
 11. The device of claim 10, wherein the driedreagent comprises an additive that slows the rehydration rate of thedried reagent coating.
 12. The device of claim 11, wherein the additiveis sucrose.
 13. The device of claim 1, wherein the dried reagent coatingcomprises sucrose, polyethylene glycol, and a dye-labeled antibody. 14.The device of claim 1, wherein the inlet port has a volume greater thanthe volume of the fluidic channel.
 15. The device of claim 1, whereinthe walls of the inlet port and the walls of the fluidic channel arecoated, at least in part, with a hydrophilic layer.
 16. The device ofclaim 15, wherein the walls of the inlet port and the walls of thefluidic channel are hydrophilic having a water contact angle of lessthan 50 degrees.
 17. The device of claim 1, further comprising a lidthat irreversibly closes over the inlet port.
 18. The device of claim 1,further comprising an internal tilt relative to a level orientation,said internal tilt being sufficient to drive flow of said liquid samplefrom the inlet port to the outlet port.
 19. The device of claim 11,wherein said tilt is at an angle between 2 and 45 degrees relative to alevel orientation.
 20. The device of claim 1, wherein the distancebetween the wicking pad and the outlet port is sufficient to preventsaid wicking pad from draining the fluidic channel.
 21. The device ofclaim 1, wherein said wicking pad is made of a material having a wickingrate of between 10 and 200 seconds per 4 centimeters (cm) of saidmaterial.
 22. The device of claim 1, wherein said wicking pad is made ofa material having a certain absorbance rate, wherein the surface tensionof the liquid sample emerging from the outlet port breaks the fluidicconnection between the wicking pad and the outlet port when saidabsorbance rate exceeds the rate at which said liquid sample emergesfrom the outlet port, thereby preventing further fluidic flow from theoutlet port to the wicking pad.
 23. The device of claim 1, wherein saiddistance between the wicking pad and the outlet port is between 1 and 5mm.
 24. The device of claim 1, wherein the detection region comprises aplurality of capture molecules bound to the inner surface of the firstsubstrate.
 25. The device of claim 1, wherein the plurality of capturemolecules are arranged as an array including at least two reactionsites, each of the at least two reaction sites being formed bydepositing a composition onto the inner surface of the first substrate,said composition comprising at least one of the capture molecules. 26.The device of claim 25, wherein binding of the dried reagent to theanalyte in the sample does not prevent binding of the same analyte tothe plurality of capture molecules.